Struggling with messy data pipelines, slow ETL processes, or unreliable reports? I help businesses build clean, automated data infrastructure so your team can trust the numbers and make faster decisions. Whether you need a pipeline built from scratch, a broken ETL fixed, or a migration to the cloud, I get it done reliably and on time. Tech stack I work with: Python, SQL, Apache Airflow, dbt, Apache Spark, PostgreSQL, BigQuery, Snowflake, Redshift, AWS (S3, Glue, Lambda), GCP, Docker, Kafka, REST APIs. Projects I have worked on: E-commerce pipeline: built an automated ETL process handling 10M+ daily records and reduced reporting delay from 24 hours to under 1 hour. Data warehouse migration: moved legacy SQL Server to Snowflake and rewrote 50+ queries into clean dbt models. Fintech streaming: designed a Kafka and Spark pipeline for real-time transaction processing with low latency. Skills used: data modeling, pipeline optimization, cloud infrastructure, scheduling, monitoring, and documentation. If you have a data problem you are not sure how to solve, just send me a message. I will reply within a few hours with a clear plan.

Hi I am Thabo Arthur Masondo Azure fabric data engineer I AWS-Certified Data Engineer | Pipelines, Automation & certified AWS Solutions architect Data Engineering ETL / ELT SQL Python AWS (S3, Glue, Lambda, Redshift) Data Pipelines Data Modeling Data Warehousing API Integration Apache Airflow DBT (Data Build Tool) Cloud Architecture Data Cleansing Automation Git / GitHub CI/CD Power BI PostgreSQL MySQL Big Data Processing Built an automated data pipeline that collects sales data from an API, stores the raw data in S3, cleans and transforms it using AWS Glue, and loads it into a structured data warehouse. Created an automated refresh schedule and a Power BI dashboard for daily reporting. What I Delivered: Automated ingestion from REST API Data cleaning using Python & PySpark Data Warehouse with fact & dimension tables Daily refresh scheduling Power BI dashboard for insights Logging, error handling & monitoring

I help businesses unlock the power of their data, by building cloud pipelines that fuel AI, machine learning, and advanced analytics. I’m a Data & AI Engineer with expertise in Google Cloud, Microsoft Azure, Databricks, and Snowflake, helping businesses go beyond just collecting data—I help them use it for predictive insights, automation, and decision-making with AI/ML. From scalable data pipelines to machine learning workflows, I design solutions that are production-ready, cost-efficient, and built for growth. 🔹 Cloud Data Engineering Built data lakes & warehouses on GCP (GCS + BigQuery) and Azure (ADLS + Synapse/Snowflake) Designed real-time streaming pipelines with Pub/Sub, Dataflow, and Kafka Automated transformations using dbt + SQL for business-friendly analytics Orchestrated workflows with Composer & Azure Data Factory 🔹 AI & Machine Learning Enablement Developed and deployed ML models using Databricks (PySpark + MLlib) Integrated BigQuery ML for lightweight machine learning at scale Built end-to-end MLOps pipelines with CI/CD for reproducible models Worked with prediction, classification, and recommendation systems using Python & cloud-native tools Designed data pipelines that feed ML models to ensure clean, reliable training data 🔹 Business Intelligence & Visualization Built interactive dashboards in Power BI and Looker Studio Connected models to BI tools for AI-driven reporting & forecasts Delivered data storytelling solutions that make insights easy to act on 🔹 Governance & Modernization Migrated legacy systems (SSIS) to modern cloud-native solutions Applied data governance with Unity Catalog and secure credential management Automated workflows with GitHub Actions & MLOps practices 💡 Bottom line: Whether you need a robust data platform, AI/ML pipelines, or predictive dashboards, I help you build future-proof solutions that turn raw data into real business value. 👉 Let’s connect and build scalable data + AI solutions that work for you.

Senior Data Engineer | Full-Stack Data Architect | 5+ Years | Healthcare, SaaS & Analytics Hi, I'm Sundaresan — a Data Engineer who builds and owns the entire data stack, not just a part of it. I've designed end-to-end pipelines, real-time architectures, analytics layers, and ML infrastructure from scratch — and I've done it in high-stakes environments like tele-ICU healthcare and Series A SaaS startups. I don't stand on the sidelines with a list of suggestions. I get into the arena and build. - Trusted By: Teams at Cloudphysician, Toplyne (Peak XV / Sequoia-backed), and Swiggy — across healthcare, developer tools, and consumer tech. - Impact Delivered: 1M+ events/day pipelines, 99.9% reliability, 3–5× query performance improvements, $X million incremental revenue identified through ML-driven analytics. - Approach: I join as a full-stack data owner — data engineer, analytics engineer, and analyst rolled into one — and stay until the problem is solved. 🟢 Technical Skills - Designed and owned end-to-end data pipelines — CDC, streaming, and batch — with sub-5-minute latency at production scale - Built unified data architectures (BigQuery, Snowflake, AWS) with medallion architecture and clean separation of concerns - Established product analytics infrastructure covering 90%+ of user interactions across web and mobile - Built real-time ML model monitoring frameworks for 100+ models using MLFlow, Airflow, and Grafana - Worked with HL7, FHIR, ICD and SNOMED codes — HIPAA and ABDM-compliant clinical data pipelines - ETL and data science pipeline migration to PySpark, reducing infrastructure costs by 30% LLM integration in production: Gemini Flash for document parsing, speech-to-text transcription, and ambient AI note-taking - Power user of dbt, Metabase, Looker, Grafana, and modern data stack tooling - Proficient in Python, SQL, Spark, Airflow, and workflow automation (n8n) 🟢 Soft Skills - Clear, direct communicator — comfortable working with doctors, product managers, and engineers alike - Neurotic about data model design — I will push back if the schema is wrong - Full ownership mentality: I treat your data infrastructure as if it were mine - Strong documentation practices and data governance discipline - Proactive in surfacing insights, not just shipping pipelines 🟢 Roles and Responsibilities - Design, build, and own data pipelines from ingestion through to BI and ML consumption - Establish data infrastructure from scratch — empty cloud project to production-grade ecosystem - Partner with product, engineering, and clinical/business teams to translate requirements into data models - Build and maintain KPI dashboards that reduce reporting time from days to minutes - Deploy and monitor ML pipelines in production with automated alerting and drift detection - Ensure compliance with healthcare data standards (HIPAA, HL7, FHIR, ABDM) - Mentor and scale data teams; establish best practices and coding standards 🟢 Conclusion With 5+ years building the full data stack across healthcare and SaaS, I specialise in taking messy, fragmented data environments and turning them into reliable, low-latency systems that teams actually trust. Whether you need a pipeline built from zero, a slow warehouse optimised, or an ML monitoring framework standing up — I own it end-to-end. Clean data, fast queries, and systems that don't break at 3am. That's what I deliver.

I help companies migrate their legacy data warehouses to modern Snowflake architectures — fast, clean, and production-ready. With 10+ years building enterprise data platforms for pharma, fintech, and SaaS companies (Twilio, Regeneron, Sun Life Financial), I specialize in: ✓ Legacy-to-Snowflake migrations (Informatica IICS, PowerCenter, on-prem databases) ✓ Snowflake setup & optimization (Iceberg tables, Streams, Tasks, RBAC, cost tuning) ✓ dbt pipeline development (incremental models, testing, documentation) ✓ AWS integration (S3, Athena, Redshift, Glue) ✓ Data quality frameworks & validation Recent projects: - Migrated 40+ product pipelines from IICS to Snowflake Iceberg + dbt at Twilio, cutting runtime by 45% - Managed daily ingestion of 100+ feeds into AWS Redshift for US pharma client (99%+ SLA) - Built Snowflake EDW star schemas and Workday integrations for global insurance firm I don't just write code — I deliver working systems. If you're stuck migrating off Oracle/SQL Server/Teradata, or your Snowflake setup needs optimization, let's talk. Available for: - Fixed-scope migration projects (4-8 weeks) - Ongoing Snowflake support (retainer basis) - Emergency data pipeline fixes Work hours: Flexible across US/EU/India timezones Communication: Daily updates, Slack/email, documentation included

🧠 Building production-grade agentic analytics systems that turn natural language into governed, CFO-trustworthy answers directly from live data warehouses. 🛠️ Specializing in: Semantic Layer Design | NL2SQL Systems | LLM Evaluation Frameworks | Data Agent Architecture | Warehouse Modeling 🇺🇸 Production-focused AI/Data Engineer with 7+ years building LLM-over-structured-data systems across cloud data warehouses and modern ELT stacks in AU/US/Canada enterprise environments. 🏆 Key Achievements: ● Built a production NL2SQL AI analytics agent used by enterprise finance and ops teams, reducing manual BI queries by 70% while maintaining <1% metric deviation vs certified reports. ● Designed a governed semantic layer (metrics + entity graph + glossary) ensuring single-source definitions for revenue, margin, utilization, and cohort KPIs across multi-region warehouses. ● Developed an LLM evaluation & reconciliation framework (golden datasets + regression suites + LLM-as-judge + SQL diffing) that caught and prevented critical hallucinated revenue overstatements in production. ● Implemented retrieval-augmented schema grounding (vector + metadata search) to eliminate incorrect join-path selection in NL2SQL pipelines. ● Led migration of legacy BI logic (Power BI/DAX/SSRS) into dbt + semantic layer models, improving consistency and reducing reporting discrepancies by 80%. ⭐️ Client Testimonials: “He completely eliminated our BI inconsistencies. We finally trust our AI-generated numbers.” “Exceptional depth in SQL and LLM systems—he found issues our data team missed for months.” “He doesn’t just build AI agents—he builds systems that are actually reliable in production.” 🎓 Computer Science background with deep specialization in data systems, LLM orchestration, and cloud data platforms. I focus on one core principle: 👉 If the number is wrong, the system is wrong—not the prompt. My work spans the full stack of agentic analytics: ● Semantic layer design (metrics, entities, governance) ● Context engineering for structured data (RAG over schemas, docs, and query history) ● Robust NL2SQL pipelines with join-path validation ● Evaluation systems (golden datasets, drift detection, regression testing) ● Data warehouse modeling (dbt, medallion architecture, ELT best practices) I’ve worked extensively with modern data + AI stacks including Snowflake, Databricks, dbt, vector search systems, and LLM orchestration frameworks. 🌟 Why Choose Me: 🔒 Trustworthy analytics systems (no hallucinated metrics) 🧩 Deep semantic modeling expertise (not just prompt engineering) 🧪 Eval-first engineering mindset (everything measurable, testable, regression-safe) ⚙️ Production-grade SQL + data architecture skills 🧠 Strong intuition for failure modes in NL2SQL systems 💡 Specialties: NL2SQL Systems, Semantic Layer Design, dbt, Snowflake, Databricks, SQL Optimization, Python, LLM Agents, LangChain, LlamaIndex, Vector Databases (pgvector), MCP, Data Contracts, Data Governance, Data Quality Testing, ELT Pipelines, Warehouse Modeling, BI Migration, Power BI/DAX Reverse Engineering, Observability, RAG Systems, AI Evaluation Frameworks

I'm a Senior Data Engineer with 10+ years of experience designing, building, and optimizing scalable cloud data platforms for global enterprises. I specialize in developing high-performance ETL/ELT pipelines, modern data warehouses, and real-time streaming solutions across Google Cloud Platform (GCP), Microsoft Azure, and AWS. I help organizations modernize their data platforms, migrate legacy ETL systems to the cloud, optimize Spark and SQL workloads, and build reliable, analytics-ready data pipelines that improve performance while reducing operational costs. My expertise includes: ✔ ETL/ELT Pipeline Development (PySpark, Python, SQL) ✔ Cloud Data Engineering (GCP, Azure, AWS) ✔ BigQuery, Snowflake & Databricks ✔ Apache Spark & PySpark Optimization ✔ Azure Data Factory, Airflow & Cloud Dataflow ✔ Data Warehousing & Dimensional Modeling ✔ dbt, Lakehouse & Medallion Architecture ✔ Kafka & Pub/Sub Streaming Pipelines ✔ CI/CD, DataOps & Data Quality ✔ Cloud Migration & Platform Modernization What I can help you with: • Design scalable cloud data platforms • Build high-performance ETL/ELT pipelines • Migrate legacy ETL tools to modern cloud architectures • Optimize PySpark, SQL, and BigQuery performance • Develop real-time streaming data pipelines • Implement enterprise data warehouses • Reduce cloud costs and improve pipeline reliability I believe in writing clean, maintainable, and production-ready code while delivering solutions that are scalable, cost-effective, and aligned with business goals. I value clear communication, timely delivery, and long-term client relationships. Let's discuss how I can help build or modernize your data platform.

I am a Big Data Engineer with 2.5+ years of experience building production-grade data platforms handling Terabytes of data. Currently working at dunnhumby (a global leader in customer data science), I specialize in building robust, scalable data infrastructure that doesn't break. Why work with me? Enterprise Grade: I don't just write scripts; I build containerized, tested, and CI/CD-ready pipelines. Scale Ready: My daily work involves Apache Spark, Flink, and Kafka. I am comfortable with data volumes that crash standard scripts. Cloud Native: Deep expertise in Google Cloud Platform (BigQuery, Dataproc, GCS) and AWS. Core Competencies: Languages: Python (Expert), SQL (Advanced), Java. Big Data: Apache Spark, Flink, Kafka, Iceberg. Infrastructure: Docker, Kubernetes, Terraform. I hold a B.Tech from NIT Jalandhar and bring the discipline of a Tier-1 engineering graduate to your project. Whether you need to fix a broken pipeline or architect a new warehouse from scratch, I can help.

I'm the person you bring in when your team has data but no one trusts the dashboards, or has dashboards but no one uses them. Two and a half years on BigQuery + dbt as the core stack, with Looker Studio and QuickSight on top, and Redshift experience on the AWS side. AI shows up in two distinct places in my work: inside the team to ship faster and with fewer errors, and on the consumption side via MCP so non-technical users can self-serve. Currently leading the data work at a company selling across sales ops, inventory, attendance, and ticketing/B2B. What I'm useful for: * A dbt + BigQuery project from scratch, or untangling one that's grown organically and lost trust * Dashboards that answer the questions your team actually asks in standups, not the ones a template assumes * Exposing analytics through APIs when an internal tool or product needs trusted numbers without a BI dashboard in front * Self-service / agentic analytics via MCP, so non-technical teammates and clients can ask data questions in plain language instead of filing tickets and waiting on a one-off report * Automations and AI integrations inside the data team's own workflow (CI tests, generated docs, AI-assisted modeling and review), so the team ships faster and with fewer errors * Cost work on existing BigQuery setups (most of what I see is running 2-3x what it needs to) Domains I know well: * Sales and revenue: funnels, conversion, cohort retention, simple forecasting * Inventory and ops: turnover, stockouts, replenishment * Workforce: shifts, presence, productivity * Tickets and B2B: visitor cohorts, LTV, retention curves How I work: short loops, written updates, plain language with non-technical stakeholders. I ask a lot of questions about the business before touching the warehouse, which usually saves a week or two of building the wrong thing. To start a conversation, send: 1. What you sell and to whom 2. One or two decisions you keep making blind because the data isn't there yet I'll come back with a concrete plan for what to build first and what to skip.

I've spent 15+ years as a Senior Data Platform Engineer building and maintaining production reporting systems for multi-tenant SaaS and industrial environments. I help data-intensive SaaS companies modernize and stabilize large database systems by optimizing slow SQL, execution plans, indexing strategies, ETL workflows, and warehouse performance across PostgreSQL, MySQL, Snowflake, and SQL Server. My strongest work is in performance-heavy environments where reporting, analytics, pricing logic, monitoring, and automated decision-making depend on clean, fast, reliable data. I have optimized long-running production queries, redesigned indexing and archival strategies, improved ETL/data synchronization flows, and supported modernization of large operational databases without disrupting live products. 𝗪𝗵𝗮𝘁 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝘀𝗮𝘆: "Optimized our data models and indexing strategy, cutting report generation time while rolling out to new clients with zero issues." "Built and owned the automated publishing pipeline that now reliably serves our full client reporting fleet." "Clear ownership and proactive monitoring during our platform modernization - exactly the engineer we needed." 𝗦𝗤𝗟 𝗦𝗲𝗿𝘃𝗲𝗿 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 ✔ Design and optimize complex queries, views, stored procedures, and data models for large-scale production reporting ✔ Execution plan analysis, indexing strategy, and performance tuning across multi-tenant environments ✔ Lead SQL Server version migrations and schema modernization with full compatibility and regression testing ✔ Maintain and extend optimized view/indexing architectures mid-rollout while preserving stability 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 & 𝗧𝗮𝗯𝗹𝗲𝗮𝘂 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 ✔ Operate and extend GitHub-based workbook duplication/publishing pipelines using Python, REST APIs, and GitHub Actions ✔ Manage extract scheduling against production OLTP databases and minimize refresh load ✔ Maintain and deploy Tableau workbooks across dev/UAT/production supporting ~100 per-client reports ✔ Tableau Server administration, extract monitoring, and proactive root-cause resolution for failures or degradation ✔ Support smooth transition of reporting workloads to in-app platforms while keeping Tableau reliable 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴, 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗖𝗿𝗼𝘀𝘀-𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 ✔ Implement proactive monitoring (New Relic / equivalent) to detect and resolve issues before client impact ✔ Strong troubleshooting with clear communication to technical and business stakeholders on performance and risk ✔ Collaborate with Data Engineering, Infrastructure, and domain experts during platform migrations and architectural decisions 𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗛𝗶𝗿𝗲 𝗠𝗲 ✔ Full end-to-end ownership of production reporting stacks - SQL optimization, automation pipelines, and operational reliability ✔ Proven experience with multi-tenant client reporting environments and regulated industrial data (directly applicable to healthcare compliance) ✔ Deep expertise in database migrations, Python/GitHub automation, and performance tuning - exactly the skills for your SQL Server modernization ✔ Licensed P.Eng with 15+ years delivering stable, high-trust production systems with clear ownership and honest timelines I work best with teams modernizing SQL Server environments or scaling Tableau operations who need a hands-on engineer to complete remediation work, stabilize the platform, and keep reporting fast and trusted. Send me a message with your current migration status and pipeline pain points - I’ll outline exactly how I’d approach finishing the work with zero client impact. 𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀: PostgreSQL, MySQL, Snowflake, Yellowbrick-compatible warehouse tuning, execution plan analysis, query rewrites, indexing, memory tuning, ETL optimization, large data footprints, SaaS modernization, pricing analytics, eCommerce intelligence, brand monitoring data.