I build the clean, reliable data layer your team — and your AI tools — actually trust. From marketing, finance & CRM warehouses in BigQuery to real-time pipelines running at 200k+ events/sec, I've spent 15 years making data fast, accurate, and cost-effective. Most "data problems" aren't really about tools — they're about numbers finance can't trace, dashboards that silently break, and cloud bills that climb faster than the data does. I fix the layer underneath so the whole thing just runs — and often cut the bill while doing it. ⎯⎯⎯ What I do for you ⎯⎯⎯ ◆ Modern Warehouses in BigQuery (dbt + Medallion) — Unified, documented, AI-ready data from ad platforms (Meta, Google, TikTok), CRMs, and ERPs. For enhanza I consolidated financial data from 1,000+ organizations into one BigQuery platform with self-serve dashboards. ◆ AI-Ready & LLM Pipelines — Data modeled so your AI tools consume it directly: LLM enrichment/categorization, clean context windows, no manual prep. Shipped code-first with Claude Code + gcloud — no hand-written SQL in the console. ◆ Cut Your Cloud Bill (FinOps) — Runaway AWS costs are usually egress fees and over-priced managed services, not the data itself. I migrated a client's Dagster pipelines from AWS to Civo Kubernetes + Cloudflare R2 — eliminating S3 egress fees and slashing managed-cluster costs, with no orchestration rewrite. If your bill is climbing, I'll audit it and re-architect the expensive parts. ◆ Real-Time & High-Scale (when you need it) — Streaming in Flink/Kafka and sub-second OLAP in ClickHouse/StarRocks. I've run 200k+ ingest events/sec in an air-gapped cluster — so a standard pipeline is routine for me. ◆ Reliability & Observability — Monitoring, lineage, and schema-drift alerts so dashboards stop breaking. On one platform this cut data downtime by ~90%. ⎯⎯⎯ How I work ⎯⎯⎯ ▹ Root-cause, not patches. I fix the layer underneath so the same dashboard doesn't break again next month. Quick patches are how data platforms rot. ▹ Built to run without me. No "works once on my laptop" scripts — proper deployment, secrets, logging, alerts, and infrastructure as code (Terraform). Reproducible, and yours to keep. ▹ Straight about what I know. If something's outside my wheelhouse, I say so up front and give you the honest trade-off. I won't learn on your budget and call it experience. ▹ I own the outcome. Give me the goal and I'll work through the ambiguity and keep you posted — no micromanagement needed. ▹ A full team behind me. I run with a ~20-person team across data engineering, BI, DevOps, and AI/ML — so you're never betting on one person's calendar or blind spots. You work with me directly, and I pull in the right specialist when the scope calls for it. Senior ownership, agency depth. ⎯⎯⎯ Tech I work in daily ⎯⎯⎯ ▹ Warehouse & Analytics: BigQuery, dbt, dlt, Meltano, Medallion Architecture, Power BI, Looker Studio, Metabase ▹ Cloud & Orchestration: GCP, AWS, Azure, Civo, Airflow, Prefect, Dagster, Kubernetes, Terraform ▹ Streaming & Performance: Kafka, Flink, Spark/PySpark, ClickHouse, StarRocks, Iceberg, Delta, Polars, Cloudflare R2 ▹ AI-first delivery: Claude Code + gcloud/CLI, LLM enrichment pipelines ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ I don't just "install tools" — I design for compute cost (FinOps) and data reliability. If your cloud bill is climbing or your reporting can't be trusted, I'll audit the stack and tell you exactly what to fix. Want a quick, no-pressure look at your setup? Send me a message and I'll share where I'd start.

I build the systems that run underneath a business — data platforms, automation, and the AI layer that ties them together. I think in systems, not scripts: how the whole thing fits, what to build vs reuse, and where it breaks at scale. What I build Real-time and streaming data platforms for SaaS and fintech, where latency and reliability actually decide things AI systems and agents on top of that data — that summarize, decide and act, not just draw a chart Automation that pulls from messy sources, reasons with an LLM, and produces the finished output end to end RAG knowledge bases that give an AI real context on a business Recent work (names withheld) Multi-year managed real-time platform for a forex/CFD brokerage — 10-node ClickHouse, Kafka and CDC, tens of millions of events Real-time analytics rebuild for a European e-commerce analytics company, architected for 100x growth, with sub-5-second dashboards Data backbone for a fast-growing AI startup BigQuery to ClickHouse migration for a US lead-gen business Clients range from enterprise-scale operations to venture-backed startups, including one that's raised hundreds of millions Where I come in Pipelines that have quietly stopped scaling Batch reporting that needs to run in real time An AI system that needs to sit on real, reliable data Several data sources and no unified architecture How I work Architecture first, code second Reliable under real load, properly monitored, and still maintainable a year later Pragmatic about tools — the tool is never the point, the system is Stack Data & warehouses: Snowflake, BigQuery, ClickHouse, Postgres Streaming: Kafka, CDC, event-driven pipelines Engineering & orchestration: Python, dbt, Airflow, Dagster, Prefect AI: Claude, OpenAI, Gemini, RAG, vector databases, MCP Cloud & ops: AWS, GCP, Docker, Grafana, Prometheus If you're building a real-time data platform, an AI system that needs to sit on real data, or scaling pipelines that have started to hurt — tell me what your setup looks like and I'll map out how I'd approach it.

I am a tech-agnostic data engineer who enjoys solving complex data problems, finding patterns in messy datasets, and making sure the work actually supports the business. My focus is always on clean architecture, performance, and making data useful - not just moving it around.Here is what I have worked with: - Programming & Scripting: Python (Flask, FastAPI, Django, Selenium, PySpark), SQL - Data Engineering & Integration: Talend, Mage.ai, Apache Airflow, Airbyte, Databricks, Kafka, Debezium - Databases & Data Warehousing: Data Modeling, PostgreSQL, MySQL, Snowflake, Greenplum, MongoDB, Firebase, ClickHouse - Cloud & DevOps: AWS (Lambda, API Gateway, S3, RDS, SQS and more), Docker, Kubernetes, Azure DevOps, Terraform, CloudFormation - Security & Compliance: Secure and compliant pipeline design (HIPAA, GDPR, SOC 2), data governance, privacy-first architectures - Testing & Automation: Postman, Cypress.io, Swagger, Automated Data Workflows - Data Visualization: Power BI, Metabase, Google Data Studio, Grafana - Collaboration & Leadership: Cross-functional team mentoring, process optimization, data-driven decision-making

Your dashboards are slow, your data sources don't agree with each other, and leadership has stopped trusting the numbers, I rebuild the analytics layer underneath, using dbt on Snowflake or BigQuery, so your team makes decisions on data you can actually defend. With over 11,000 hours logged and 80+ projects delivered on Upwork, I've worked with growth-stage SaaS startups, fintechs, retail and eCommerce operators, real estate firms, and global research organizations to design dbt-based data models, build end-to-end ETL/ELT pipelines, and deliver BI systems that eliminate reporting delays. A few projects I've led recently: CGIAR - Built a centralized enterprise analytics platform on Microsoft Fabric and Power BI, replacing fragmented reporting across a global research consortium. Sojo Industries - Designed a continuous intelligence layer delivering real-time visibility and enterprise-grade analytics for retail operations. Markets4U - Migrated a fast-growing fintech off Metabase, Oracle BI, and Vertica onto a high-performance analytics stack built for real-time decisions. Westwise - Centralized fragmented data sources to power marketing and sales insights for a legal services platform. ECAP - Delivered analytics and financial modeling for a private real estate investment firm managing strategic property portfolios. Voice AI Automation Company - Integrated Stripe, HubSpot, GA4, Google Ads, Linear, Pylon, and MongoDB into a unified, business-ready data model for an AI-first SaaS company. How I work across the analytics engineering stack: Data Modeling and Transformation - I build dbt models on Snowflake and BigQuery that turn raw, multi-source data into clean, analytics-ready datasets. Strong focus on testing, documentation, and modular design so your data layer stays reliable as it scales. Data Engineering and Pipelines - I design and maintain ETL/ELT pipelines using SQL, Python, and Apache Airflow across AWS, GCP, and Azure. Whether you need to ingest from APIs, sync SaaS tools, or move data between warehouses, I build pipelines that don't break when your business grows. Business Intelligence and Dashboards - I build interactive dashboards in Power BI, Tableau, and Looker Studio that turn analytics-ready data into clear, actionable insights for stakeholders. KPI tracking, executive reporting, self-service analytics — designed for the people who actually use them. Cloud and Warehouse Expertise - Deep work across Snowflake, BigQuery, PostgreSQL, and Redshift, with hands-on experience integrating GA4, HubSpot, Salesforce, Stripe, and other SaaS data sources into unified warehouses for cross-functional reporting. Why clients keep working with me: - 11,00+ hours logged, 100% Job Success Score, Top Rated Plus - End-to-end ownership from data ingestion through dbt modeling to dashboard delivery - I translate technical complexity into business outcomes, not jargon - Long-term clients rehire me for scale-up support, audits, and platform migrations - Available for project-based work and ongoing analytics engineering partnerships If your reports are slow, your data sources don't talk to each other, or you can't trust the numbers in your dashboards — let's talk. I'll audit your current stack, identify what's broken, and rebuild the foundation your team needs to make confident, data-driven decisions. Send me a message and I'll map out a plan tailored to your analytics, data engineering, or dashboard work — clear scope, real outcomes, no fluff.

I solve problems, redesign systems when they reach its limits, and reimplement the core of your business to serve for years ahead.

Hands on Data architect & Lead data engineer, with 12+ years of experience in designing & building end to end high velocity, high volume peta byte real time & batch data platforms from scratch on clouds & on-prem. Kubernetes native development from the beginnig. I develop distributed & scalable back-end systems using using languages like goLang, Rust & python. Lately Started working on integration of AI, RAG, MCP Servers & MLops Platforms into data platforms. Developed self hosted llms applications using Ollama and llm observability using langfuse. Mordenize existing data platforms with AI first approach. Hybrid semantic mapping layers or unstructured and structured data using heuristics, memory and LLMs. Built & worked on peta byte scale streaming, batch data & AI platforms in top companies. An open source contributor to data technologies & products like Airbyte etc. Love working on database internals, performance and optimizations. I have experience working with telemetry data, payments data, video data, sports data, eCommerce data & affiliate marketing data, logs data, clickstream data. Skill Set: Big Data Technologies: Spark, Kafka, Flink, Presto, Dremio, Hudi, Deltalake Data warehouses: Snowflake, Druid, Clickhouse, Redshift, SingleStore(Memsql), Quest Databases: Postgres, Mysql, Cassandra, DynamoDB, DuckDB Programming languages: Golang, Python, Rust, Scala, Java Visualization: Tableau, Apache Superset, Zoomdata Data Technologies - Airbyte, Fivetran, Dagster, Airflow, Nifi, Kubeflow, ElasticSearch, OpenSearch Platforms: Databricks, Snowflake, Cloudera, Supabase, Aiven Ops: Kubernetes, Docker Cloud: AWS, GCP, Azure

Audit | Google Tag Manager | Google Analytics 4 | Server-side tagging | Data Analytics | Data Visualization Welcome to my profile. I’m a software engineer who understands analytics from the inside. I don’t just configure GA4, GTM, and pixels. Where a typical tracking specialist only works through the interface, I can go deeper: build custom GTM templates, implement server-side tracking, connect APIs, design ETL pipelines, set up data warehouses, and fix the real causes of data loss, duplication, and tracking errors. I deliver analytics end-to-end: from audit and architecture to implementation, debugging, data storage, processing, and reporting. You get not a set of disconnected configurations, but a reliable analytics system that actually helps your business make decisions. I’ve been working in software development since 2007. Senior level in Golang, PHP, Python, and C++. Former team lead. For the last 4 years, I’ve been focused on marketing data, web analytics, server-side tracking, ETL, and data infrastructure. 🛠️ I Can Help With 📈 GA4, GTM, DataLayer setup and debugging 📈 Meta Pixel + Conversion API 📈 TikTok Pixel + Events API 📈 Pinterest Pixel + Conversion API 📈 Google Ads tracking: web + server-side 📈 LinkedIn Insight Tag 📈 Microsoft Ads tracking 📈 GDPR / cookie consent setup 📈 Enhanced Ecommerce and conversion tracking 📈 Event tracking: forms, scrolls, clicks, videos, custom events 📈 Custom GTM templates 📈 Server-side tracking architecture 📈 API integrations 📈 ETL, mapping, validation and data processing 📈 BigQuery, PostgreSQL, ClickHouse, Snowflake 📈 Dashboards and reporting 🔍 Audit First: Not sure what is broken in your tracking or what should be improved first? Start with an audit. I can review your current analytics setup and provide a clear report with: ✅ how your tracking system works today ✅ where data is being lost, duplicated, or misattributed ✅ what should be fixed first ✅ what can be improved or added ✅ what architecture will work better for your business 🤝 What You Get: You are not locked into implementation with me after the audit. You can use the report to compare approaches, validate other opinions, and make the right decision for your business. Many clients continue working with me long-term because they need more than setup - they need a technical analytics partner. Send me a message, and I’ll tell you what kind of audit makes sense for your setup.

Fractional CDO and Senior Data Engineer for Series A-B SaaS, marketplaces, and consumer-tech. Builds full-stack data platforms end-to-end: warehouse (Snowflake, BigQuery, ClickHouse), transformation (dbt, Fivetran), BI (Power BI, Looker). 56 Upwork contracts closed, 5+ ongoing 1-3 year retainers including Tools for Humanity (Worldcoin), BHL, and OpenTag. Service menu: • Data strategy and roadmap (from Series-A "we need data" to Fortune-100 architecture) • Warehouse stand-up on Snowflake, BigQuery, or ClickHouse — from scratch or migration • Modern data stack: dbt models, Fivetran ingestion, Airflow orchestration, semantic layer • BI implementation and audit (Power BI, Tableau, Looker, Looker Studio, Metabase, Superset) • Analytics team hiring, mentoring, KPI stewardship, data governance • Customer Data Platform and event tracking (GA4, server-side GTM, attribution) Recent engagements and rates: • ClickHouse Expert, $176/hr, 7 months. 12B rows/month, query p95 14s to 380ms. • Data Consultant CDO, $120/hr, 8 months. Built the data org from scratch for a Series B fintech. • Database and Cloud Architecture Consultant, $125/hr. • Looker Studio migration, $115/hr. 40 Sheets dashboards migrated, weekly reporting time cut 70%. • Long-term retainers: OpenTag Superset ($90/hr, 13 months), BHL Metabase ($80/hr, 13 months), Tools for Humanity Worldcoin ($75/hr, 2.5+ years). Where I fit best: • You have raw data in production but no unified view for the team • Your BI is fragmented across Sheets, Excel, and three half-built dashboards • Your gap is senior architecture, and hiring more junior engineers won't close it • You want a decision partner who thinks in unit economics Where I don't fit: • Pure ML/CV/NLP research (LLM + RAG for data pipelines is fine) • Marketing/CRO tracking without an analytics stack under it • Junior data-entry or dashboard-copy tasks Communication: async-first, Slack + Loom + written docs. CET timezone, US-friendly hours daily. Send a message with your stack, one business question, and the number you want to move. Reply within 8 hours business days.

I build the data infrastructure that makes LLMs actually work in production, RAG pipelines, vector search, and AI agents that don't hallucinate on your company's real data. Before AI, I spent 8 years as a Data Engineer building pipelines with [Python/SQL/Spark/Airflow/dbt, your actual stack]. That background is exactly why my AI systems don't fall apart at scale: I know how to move, clean, and structure data before an LLM ever touches it, which is where most "AI integrations" quietly fail. What I do: → Design and build RAG systems (retrieval-augmented generation) using Pinecone/Weaviate/pgvector/Chroma → Build production LLM pipelines with [LangChain/LlamaIndex/OpenAI API/Anthropic API] → Design embedding pipelines and vector databases that scale past prototype stage → Build AI agents and chatbots that connect to real business data, not toy demos → Handle the "boring" parts most AI freelancers skip: chunking strategy, latency, cost optimization, evaluation Recent results: ✅ "Cut RAG query latency by 60% on a 10M-vector index" ✅ "Built a support chatbot that reduced response time from 4 hrs to 90 seconds" ✅ "Migrated a client's AI prototype into a production pipeline handling 50K docs" I work best with: - Startups building their first AI product who need it to actually ship - Companies with an existing data stack who want to layer LLM capability on top - Teams whose "AI feature" broke in production and need someone who understands both data and models If your project involves LLMs touching real company data not just prompt-tweaking, let's talk. Send me your use case and current stack, and I'll tell you honestly whether RAG is even the right approach for it.

Data Engineer building the full-stack intelligence layer for modern businesses. I architect, implement, and maintain the complete data-to-insights value chain. I deliver three interconnected pillars: Reliable Data Infrastructure: Scalable pipelines, cloud data warehouses, and ELT processes. Actionable Business Intelligence: Dashboards, reports, and self-service analytics that inform strategy. Intelligent Automation: Production AI/ML models, RAG systems, and LLM integrations that predict outcomes and automate complex tasks. With deep expertise across the modern data stack (AWS/Azure/GCP, Snowflake, Databricks, Kafka, Airflow, dbt, Power BI/Tableau, Python ML stack, RAG/LLMs), I ensure your data assets are operational, insightful, and intelligent.