Looking for a Python developer with sports analytics experience to build a college football grading engine. The full specification includes 34 custom metrics, requiring a deep understanding of football analytics and data modeling. The project involves creating a grading engine that can process large datasets efficiently and accurately. Ideal candidates will have experience with data visualization and machine learning.

We are looking for a Python developer to help with a short-term project involving AWS integration and SP API development. The ideal candidate should have experience building API integrations, automation scripts, and cloud-based workflows. Responsibilities: - Develop Python scripts for API integration and automation. - Integrate with SP API and handle data exchange workflows. - Work with AWS services (Lambda, S3, EC2, IAM, etc.). - Manage API authentication, requests, responses, and error handling. - Debug issues and optimize existing workflows. Requirements: - Strong Python programming skills. - Experience with REST APIs and JSON data handling. - Hands-on AWS experience. - Experience with SP API or similar marketplace APIs preferred. - Ability to deliver clean, documented, and reliable code. Project Type: Short-term contract / freelance

We are building an early-stage real estate data platform that collects, cleans, enriches, and serves public-record and legal-notice data for real estate investors and professionals. This is not a greenfield build. We already have an existing backend repo with API routes, database models, migrations, scraping workers, tests, Docker configuration, and cloud deployment pieces. We need a strong backend engineer who can step into the existing system, understand what is working, identify what is risky, and help us get the backend stable enough for launch. The right person is practical, scrappy, and comfortable working in a startup environment where the goal is not perfection. The goal is to find the highest-leverage path to a reliable product. The platform involves: -Public-record and legal-notice data -Property data enrichment -API endpoints used by a frontend application -Data quality, reliability, and launch-readiness Current Backend Stack The backend is built primarily in Python and includes: -FastAPI -SQLAlchemy and Alembic -Postgres / Google Cloud SQL -MongoDB helper/caching layer -Scraping and ETL pipeline for public-record and legal-notice data -Playwright/Patchright-based scraping -reCAPTCHA-aware scraping workflows -LLM-based data extraction / AI-assisted parsing of unstructured notice data -Pydantic models -Google Cloud integrations: Cloud Run, Cloud Scheduler, Pub/Sub, Secret Manager, Cloud Storage, Artifact Registry -Docker -Pulumi infrastructure-as-code -GitHub Actions CI/CD -pytest, Ruff, uv You do not need to be world-class in every tool listed above, but you should be strong enough in Python backend systems, scraping/data pipelines, and cloud deployment to quickly understand the architecture and make sound technical decisions. What We Need Help With We need someone who can: -Review and understand the current backend architecture -Stabilize and improve the scraping / ETL pipeline for public-record and legal-notice data -Make sure public-record and legal-notice data is collected, parsed, stored, and served correctly -Improve backend APIs used by the frontend -Improve data quality checks for incomplete, missing, or inconsistent property records -Build and maintain property enrichment workflows using external data sources -Help design database models for richer property history and event tracking -Improve LLM-assisted parsing of unstructured legal notice data where appropriate -Debug deployment, CI/CD, Cloud Run, and infrastructure issues -Improve logging, error handling, monitoring, and observability -Strengthen test coverage where it matters -Help document the backend so future developers can contribute -Coordinate with our frontend developer to support product launch -Help prioritize backend work based on launch impact, data reliability, and technical risk Who This Is For You are likely a strong fit if you: -Like working inside existing codebases -Can diagnose messy systems without needing everything rewritten -Think in practical tradeoffs, not just ideal architecture -Are comfortable with incomplete documentation -Have experience with scraping/ETL workflows and unstructured data extraction -Can explain technical risks clearly to a non-technical founder -Prefer shipping useful improvements over debating perfect abstractions -Are willing to own outcomes, not just complete assigned tickets Who This Is Not For This is probably not the right fit if you: -Only want clean, fully documented codebases -Prefer to rebuild from scratch by default -Need enterprise-level process before making progress -Are an agency sending rotating developers -Only want tightly defined tickets with no ambiguity -Are uncomfortable with scraping, data quality, or production debugging Hiring Process We want to keep the hiring process practical and focused on real work. 1. Initial Screening We will review your proposal, background, and screening question responses. 2. Real-World Technical Scenario Strong candidates may be asked to respond to a specific backend issue from our current roadmap. We are looking for how you think, what tradeoffs you notice, and how clearly you communicate. 3. Paid Finalist Review A small number of finalists may be invited to complete a paid review of the existing backend codebase before any larger implementation work begins. Budget / Working Style We are an early-stage company and are looking for a practical, startup-minded developer. This is a paid contract role, but we are not looking for enterprise-agency rates. We value clear communication, efficient execution, and someone who can help us prioritize the highest-leverage backend work first. The first paid technical review may be structured as a fixed-price milestone. Continued implementation work may be hourly or milestone-based depending on fit. Long-Term Opportunity Our goal is to find someone who can become a long-term backend partner for the product, not just complete isolated tickets. For the right person, there may be an opportunity to grow into a technical lead / backend ownership role with additional upside tied to company performance. We are looking for someone who wants to help take a real product to market, but the initial engagement will be paid, scoped clearly, and focused on proving mutual fit.

Experienced Dev Ops from start to finish, We are seeking a skilled Python and React Developer Expert for Web Based Trading Application Platform to create a robust and user-friendly application for both web and mobile users. The ideal candidate will have experience in developing and launching apps from start to finish. Knowledge of stocks and options is necessary or trading experience is a MUST. If you do not know how to trade stocks or options, you will be required to take a strategy class (fee paid by the applicant freelancer) with founder owner, to learn how the trading strategy works, in-order to be hired. Deliverables • Ensure existing application functionality and performance as per founder owners requirements • Strategy is proprietary and is confidential for period of 30 years • All coding is live over anydesk each day, all coding sessions are recorded over zoom

About Us We are a forward-thinking AI enterprise software company building governance solutions. Our systems combine Python engineering, Natural Language Processing, and Machine Learning to deliver secure governance solutions. We’re seeking a Back-End Python Engineer with expertise in AWS deployed applications, GITHUB CI/CD pipelines, DJANGO, ML Pipelines, Endpoint Integration, Sagemaker, containerization, Use of AI to design front end applications and debug code. Key Responsibilities Design, develop, and maintain back-end services in Python to support software application Debug Application for Quality and Assurance Build Data Connectors for Application Integration Implement new features with front end design as needed Containerize and deploy services across AWS infrastructure. Build and scale RESTful APIs and microservices (Django + DRF) that integrate into automated pipelines. Tune system performance (network, I/O, memory, GPU utilization) for optimization. Architect and maintain databases (SQL & NoSQL), ensuring query optimization, high availability, and caching (Redis). Integrate background processing (Celery) and real-time communication (WebSockets) into containerized environments. Collaborate with DevOps, front-end, and AI/ML teams to deliver end-to-end automated workflows. Apply best practices in system design (SOLID, DRY, KISS), Python standards (PEP8), and secure infrastructure deployment. Qualifications Core Skills Proficiency in Python (OOP, async, functional programming, data structures). Expert-level knowledge of AWS Infrastructure (deployment, operators, CI/CD, scaling). Strong background in containerization (Docker, Podman) and Kubernetes-native orchestration patterns. Experience supporting AI Dev automation workflows and integrating back-end services with automated pipelines. Deep knowledge of Django & DRF: ORM, serializers, view sets, permissions, HTTP methods. Advanced database design & optimization for high-throughput applications. Familiarity with Redis caching, Celery task queues, and uWSGI/ASGI communication layers. Solid testing skills (pytest/unittest) and CI/CD pipelines with Git. Preferred Expertise Hands-on experience with GPU-enabled workloads and hardware acceleration in containerized environments. Familiarity with infrastructure automation tools (Ansible, Terraform, or similar). Agile/Scrum team experience and use of task tracking (Jira, Trello). What We’re Looking For We want an engineer who: PRIORITIZES SECURITY OF SYSTEMS AND INFRASTRUCTURE ACROSS SECURITY FRAMEWORKS Builds automation-first systems that support AI Dev workflows from code to deployment. Thinks about performance and scalability at the infrastructure + software level. Collaborates across teams (DevOps, AI/ML, product) to deliver fully integrated, automated platforms.

ABOUT THE PROJECTS. I run a curated marketplace on Shopify (The Ever Good) and I am building out an AI automation system to handle routine tasks in the business. I am AI-savvy and have built a Python agent myself, so I understand what I am asking for. I have the architecture planned and the business requirements semi-documented for each piece. I simply do not have the time to build everything I want to build, which is why I need a developer to work alongside me. The first project is a product review collection and import system. If it goes well, there is ongoing work building out additional agents over time, one at a time. This is a potential long-term engagement depending on how the first project goes. WHAT WE ARE BUILDING. The overall system is a set of AI agents that handle specific business tasks and route outputs to a simple dashboard where I can review, approve, and trigger actions before anything goes live. The dashboard is a Lovable build that gives me a single place to monitor agent status, review outputs, and approve or return anything that needs a human decision before it moves forward. Agents are planned across several areas of the business. We will build one at a time. I will walk you through the requirements for each before you start. Examples of agents here by business category (these could change): - MARKETPLACE & CATALOG. Product Reviews (first project), Catalog Enrichment and SEO, Pricing and Margin Monitor, New Product Auto-Pricer, Maker Stories, Maker Audit, Maker Analytics, Review Monitor. - CONTENT & SOCIAL. Content Generation, Cultural Moment Monitor, Social Publishing, Pinterest Curator, Instagram DM Automation, Image Production, Founder Content Amplifier. - MARKETING & ADS. Email Sequences, Paid Media Director, Ads Performance Monitor, LinkedIn Outreach. - SEO and Search. SEO and AI Search Visibility, Crawl Error and Redirects. - THE SCHOOL (Our Coaching Offerings). AI Readiness Assessment, Coaching Prep Tool, Workshop Launcher, Course Completion Monitor, Immersion Round Tracker. - FINANCE & OPS. Profit Police, Cash Flow Forecaster, Financial Health Monitor, System Health Monitor. - CUSTOMER SERVICE & GIFTING. Customer Service Drafts, Gift Inquiry and Proposals, Basket Assembler. OUR TECH STACK (Could Change). - AI AGENT BUILDING STACK. Claude Code, Claude API, Python, Make.com, Replit, Lovable, Google Sheets API, Baserow. - OTHER BUSINESS SYSTEMS. Shopify API, DropCommerce API, Matrixify, Typeform, Stripe API, Yotpo API, Klaviyo API, Instagram Business API, Later API, Pinterest API, ManyChat API, Bannerbear API, LinkedIn API, Google Analytics 4 API, Google Search Console API, DataForSEO API, QuickBooks Online API, Google Drive API. HOW WE WORK. - HOURLY. Collaborative and iterative engagement. - REQUIREMENTS. We review together before each build and refine as we go. - QA. I handle testing and QA on my end. - DOCUMENTATION. All work is documented throughout with decision logs and handoff notes so full ownership and control remains with me at every stage. - IP. All intellectual property and work product belongs to me entirely. - CLAUDE. You are expected to maintain your own Claude environment at a level that supports serious development work with no usage limitations. - ACCESS. All business systems and APIs provided with scoped credentials as needed. - DEPLOYMENT. Production deployment is handled collaboratively. - COMMITMENT. Looking for someone who can help maintain and evolve these tools over time while I retain full control and understanding of everything we build. WHAT I AM LOOKING FOR. - CLAUDE. Must use Claude as your central AI LLM. Experience. Working experience with the Claude API and the tools in the building stack above, or a demonstrated ability to learn new tools quickly. - COMMUNICATION.Clear communicator who works independently and does not need to be managed through a task, but is promptly responsive to me. - MINDSET. Building with AI tools as a regular part of your work. TO APPLY. Please share a brief description of one or two AI agents you have built, what they did, and what tools you used. Include a note on your familiarity with the tools listed above and your hourly rate.

Overview We're building an open-source CLI gateway for multi-agent AI orchestration — model-agnostic, MCP-native, and designed to bring any agent framework online with a single command. The repo is active, well-documented, and growing. We need an engineer to accelerate integration coverage and help attract open-source contributors. The Work Build agent templates and runnable examples for LangGraph, CrewAI, and similar frameworks Add LLM provider support (Groq, Mistral, Gemini, etc.) to the Hermes runtime Write clean, contributor-friendly code that models good PR hygiene Submit work via fork → PR → merge workflow on GitHub You Are Strong Python developer with CLI tooling experience Familiar with at least one of: LangGraph, CrewAI, LiteLLM, LangChain Comfortable with open source GitHub workflows (fork, PR, issues, reviews) Self-directed — you read docs, ask good questions, and don't wait to be unblocked Nice to Have Experience with MCP (Model Context Protocol) Familiarity with SSE, OAuth 2.1, or agent credential management Prior open source contributions Engagement Part-time to start, 20 hrs/week Fixed milestones per integration delivered Potential to grow with the project To Apply Share your GitHub profile and one example of open source work or a project that shows your Python and agent framework experience. https://github.com/ax-platform/ax-gateway

DESCRIPTION; I'm building a data infrastructure product for ontology-driven AI context: object types, properties, and relationships materialized ahead of query time, so AI systems retrieve connected context fast instead of rebuilding it from raw sources on every request. I need experienced eyes on the ingestion foundation before anything gets built on top of it. The deliverables are fixed (below); hours are flexible — propose what you think the work honestly takes. Rate: my budget is $50–75/hr. That's a hard ceiling — proposals above that range can't be afforded and won't be considered, regardless of quality __________________________________________________________________________ WHO SHOULD APPLY A data engineer / data infrastructure engineer who understands what an ontology and a knowledge graph are and why they matter for AI systems — connected entities and relationships as first-class context, not just tables. You don't need graph database experience; you need to get why pre-materialized, relationship-aware data beats rebuilding context from raw sources on every query. If that framing clicks for you, you're the right kind of applicant. __________________________________________________________________________ THE PRODUCT, HIGH LEVEL: The platform deploys on a client's own infrastructure — we never see their data. Clients connect their data sources, define an ontology (object types, properties, relationships), and the platform materializes it across tiered storage. Later phases add a binary serve layer, SSD/RAM caching, and GPU-parallel query execution so AI systems and data applications retrieve connected context at very low latency. Target customers: companies running AI on complex connected data (security operations, healthcare, financial services) where privacy demands private deployment and speed matters. Storage note: the current prototype uses Iceberg on GCS for development convenience, but the architecture is intentionally built for any S3-compatible storage (on-prem S3, private cloud VPC, MinIO, etc.). Portability is a design requirement, not an afterthought — the platform must never be tied to a single cloud provider. __________________________________________________________________________ WHAT EXISTS TODAY: A working Python prototype: FastAPI, PyIceberg, PyArrow, Postgres, Supabase (metadata + sync ledger), GCS as the Iceberg warehouse. Architecture and design docs are provided for orientation. The cold path is functional and tested: a 31-test production suite ran against live infrastructure at 1M–5M row scale — core correctness, concurrency, failure injection (kill mid-sync, storage outages, lease expiry), idempotency/replay, rollback, a 50-sync soak, and audit checks. All passing, with a written sign-off document you'll receive. That's exactly why I'm hiring you: tests confirm behavior I anticipated. You're here for what I didn't anticipate — structural weaknesses, hidden risks, and edge cases that a test suite written by the same mind that wrote the pipeline can't catch. I'm strong on product and systems design, not low-level data engineering. The codebase is AI-assisted, and I want a professional to find what that typically accumulates. This is a prototype built from the ground up — no live client today. The goal: ensure the ingestion foundation is genuinely solid (data coming in from source correctly, at scale, repeatedly) so a scoped MVP pilot and beta release won't break under real usage. You are validating the foundation before anything gets built on top. __________________________________________________________________________ YOUR SCOPE — THE COLD PATH, END TO END Data source → validation → identity merge → materialized ontology in Iceberg on S3-compatible storage. The data connectors are in scope — they ARE Milestone 1. The platform supports exactly three ways data comes in, and your job includes confirming each one is genuinely production-grade, not just demo-grade: Postgres — full refresh and incremental watermark sync S3-compatible object storage (CSV) — currently GCS via S3 interop, but must work against any S3-compatible store (on-prem, MinIO, private VPC) Manual CSV upload — primarily for testing/onboarding For each connector, production-grade means: real error handling (bad credentials, unreachable source, permission failures, malformed/garbage data, schema drift), clear failure messages that tell a user what broke, no silent partial ingests, and sane retry/recovery behavior. If a connector swallows errors, loses rows quietly, or fails confusingly — that's exactly the finding I'm paying for. No other connectors are planned for this milestone. Three connectors that work correctly under stress beats ten that mostly work. Focus areas across the pipeline: Connectors — production-readiness and error handling as described above Identity & matching — entities staying consistent across syncs (PK merge, fingerprint mode, composite keys) Sync semantics — full refresh vs incremental watermark sync, replay idempotency, delete behavior Relationships — FK→PK edge materialization, rebuild triggers, orphan handling, stable node identity Versioning & audit — Iceberg snapshots, rollback, schema change lineage, sync ledger completeness Reliability — failure modes, partial writes, lock/lease behavior, silent wrong-data risks Code structure — dead code, duplication, coupling, fragility; source-specific logic must stay contained in each connector and never leak into the shared pipeline Explicitly out of scope: GPU execution, query kernels, binary serve formats, caching layers, query-time serving, and any new connector types — all future phases. Your scope ends at correct, versioned, audited data in Iceberg. __________________________________________________________________________ DELIVERABLES (in priority order) Prioritized written assessment — what's pilot-ready as-is, what must be fixed before a real pilot customer (with specific recommendations), and what the existing test suite missed (edge cases, risks, gaps). Active code changes — implement fixes for the highest-priority issues you find, directly in the repo. You'll have full repo access. I'm open to architecture changes and refinements as long as they're clearly explained with reasoning. A change log that teaches — for every change: what you changed, why it mattered, what it fixes or prevents, and what to watch for going forward. This isn't paperwork — I'm making a local engineering hire for the next milestone, and your write-ups become the onboarding record. Everyone who touches this codebase after you should learn from what you found. Fixes go deepest-risk-first. What you get from me: repo access, architecture/design docs, the test suite + sign-off report, and async availability for questions. __________________________________________________________________________ ***REQUIRED EXPERIENCE: 1)Production Python data pipelines 2)Apache Iceberg, Delta Lake, or Hudi (or strong Parquet/data-lake work) 3)Postgres 4)Merge/upsert, idempotency, watermark/CDC patterns Building or hardening data connectors that real users depend on************* __________________________________________________________________________ WHERE THIS CAN GO: This starts as a fixed-scope review. Separately, I plan to make my first part-time/full-time engineering hire locally (Dallas) to build Milestone 2 and beyond — SSD caching, serve layers, containerization, and microservices as the platform scales. For the right freelancer, there's opportunity to stay engaged on recurring scoped work — reviewing the foundation as it evolves and working in conjunction with that future hire. Not required, not promised — but the door is open if the work is strong. __________________________________________________________________________ *********HOW TO APPLY — READ CAREFULLY***** Answer this one question in your proposal, briefly and in your own words: "You're building a pipeline that ingests from Postgres and S3-compatible storage and materializes a connected ontology (entities + relationships) into Iceberg. How do you design the sync process to be reliable and idempotent — especially around watermarking, commits, and failure handling between steps?" Include your proposed hour estimate for the deliverables above. Get creative — attachments and notes welcome. Note on AI-generated proposals: I use AI heavily myself — but if your proposal or screening answer is clearly AI-generated boilerplate, you will be automatically rejected without consideration. I'm hiring your judgment and experience, not your ability to paste a prompt. Short, direct, human answers. __________________________________________________________________________ A NOTE ON TECHNOLOGY BOUNDARIES: ***QUICK EXAMPLE*** FastAPI and Iceberg are what the platform uses today, not permanent decisions. As the product scales, we may want to run FastAPI alongside a second framework, replace it entirely, or eventually move away from Iceberg toward a custom storage format optimized for the GPU serve layer. Those should be engineering decisions made on merit, not decisions we're forced into because the current code makes swapping painful. What I need confirmed: is the codebase modular enough that a change like that stays contained? Core business logic (validate, merge, materialize, version) should never be tangled directly with infrastructure. API routes should be thin entry points that hand off to service logic, not where business logic lives. Iceberg writes should be isolated behind a single abstraction. If those boundaries are clean, replacing or extending a technology layer is a focused engineering effort. If they're not, it touches everything and becomes a mess under deadline pressure with a full team. Flag anywhere that boundary is broken. That's a priority finding. __________________________________________________________________________ FINAL REMARKS: NDA & IP protections This engagement requires signing an NDA and IP assignments agreement before work begins; standard protections given you'll have full repo access to a pre-launch product. Documents are provided on day one; nothing unusual in them. If that's a dealbreaker, please don't apply.

About the Role: Boxplot is looking for a versatile, senior-level Data Professional to join our hands-on consulting team. This is a high-impact role where you will dive into diverse client ecosystems, solving complex problems across multiple industries. We aren't looking for someone to just "run queries"—we need a strategic thinker who can bridge the gap between sophisticated data engineering and clear business storytelling. As a remote-first firm, we value radical transparency, high-level ownership, and the ability to work autonomously. What You’ll Do: - Build production-ready dashboards and Python/Pandas analysis pipelines that drive real business decisions. - Translate vague business questions into structured technical workflows. - Act as a lead on client engagements, eventually taking full ownership of communication and project management. - Maintain high-quality, extensive technical documentation to ensure project continuity and client success. - Leverage tools like n8n and APIs to streamline workflows and integrate emerging AI technologies. - Manage and peer-review work from specialized contractors to ensure Boxplot’s quality standards are met. Technical Requirements: We are looking for a "hit-the-ground-running" expert. You should have 5+ years of experience (minimum 3) in a data-heavy role. - Advanced proficiency in SQL, Python, and Pandas. - Ability to create advanced, insight-driven dashboards in Tableau (Power BI is a plus). - Familiarity with Azure, AWS, or Microsoft Fabric is highly preferred. - Previous experience in a client-facing or agency environment is a significant advantage. More About the Role and Our Culture To thrive here, you should identify with the following: - You enjoy switching gears between projects and stay calm in a fast-paced environment. - You have a "figure it out" mentality and don't require constant hand-holding to deliver high-quality code. - You understand that in a remote environment, visibility is key. You are proactive about sharing daily progress and keeping stakeholders in the loop. - You can explain complex technical concepts to non-technical clients with ease and grace. Compensation & Benefits: - Flexibility: We offer a very high degree of autonomy. As long as you are meeting deadlines and are available during US-based timezones, you control your schedule and location. - Benefits: 401k (4% match), disability insurance, workers' comp, and a generous, flexible PTO/sick day policy. - Health Insurance: While we do not offer a group plan, we provide a negotiable stipend to help cover your Marketplace insurance costs. Interview Process: 1. Introductory Call (30 min): A vibe check to discuss your background and our culture. 2. Technical Deep Dive (30-60 min): A practical review of your technical skills and problem-solving approach. Benefits: 401(k) 401(k) matching Flexible schedule Paid time off

Technical Skills: Languages: PHP, Python, JavaScript, SQL Databases & Tools: MySQL, PostgreSQL, MongoDB DevOps & Infrastructure: AWS, Docker, Kubernetes, Terraform, CloudFormation Version Control & CI/CD: Git, GitHub Job Summary: We are seeking an experienced Senior Software Developer to join our team. In this role, you will design and develop robust integrations with CRM platforms, architect and optimize database solutions, and contribute to our cloud infrastructure as code initiatives. You will work with modern technologies including PHP, Python, SQL, and AWS while managing containerized applications, implementing CI/CD pipelines, and interfacing with third party development teams. This is a full-stack role bridging backend development with DevOps practices. Key Responsibilities: - Design, develop, and maintain CRM integrations with leading platforms such as Salesforce, HubSpot, and Actionstep - Architect and optimize database solutions, including schema design, indexing strategies, performance tuning, and backup strategies - Design and implement Infrastructure as Code (IaC) with AWS - Develop, deploy, and manage Docker containerized applications using Docker Compose and container orchestration tools - Write clean, maintainable code in Javascript, PHP and Python - Develop and execute complex SQL queries for data extraction, transformation, reporting, and ETL pipeline development - Configure and optimize AWS services including EC2, RDS, S3, VPC, security groups, and IAM policies - Implement and maintain CI/CD pipelines for automated testing, building, and deployment - Establish monitoring, logging, and alerting systems for production environments - Conduct code reviews and provide constructive feedback to team members - Troubleshoot and resolve production issues with minimal downtime, implementing reliability improvements Required Qualifications: - 7+ years of professional software development experience - Advanced proficiency in PHP, Javascript, & Python - Expert-level SQL knowledge with experience in relational database design, optimization, and data warehouse architecture - Proven experience developing CRM integrations with REST APIs. - Experience with version control systems (Git) and CI/CD pipelines - Hands-on experience with AWS services including EC2, RDS, S3, VPC, security groups, and IAM - Proficiency with Docker and container orchestration (Docker Compose, Kubernetes experience preferred) - Experience with Infrastructure as Code tools such as Terraform or CloudFormation - Knowledge of monitoring, logging, and alerting solutions (e.g., CloudWatch, ELK, Prometheus) - Preferred Qualifications - Experience with Actionstep - Experience designing and maintaining data warehouse solutions - Kubernetes experience for container orchestration at scale - Experience with serverless architectures (AWS Lambda) - Contribution to open-source projects - Bachelor's degree in Computer Science, Software Engineering, or related field