نبذة عني
Data Engineer with 4 years of production experience building data platforms on Azure Databricks, Snowflake, and Delta Lake. Delivered ETL/ELT pipelines processing millions of records daily for Fortune 500 clients across …
Data Engineer with 4 years of production experience building data platforms on Azure Databricks, Snowflake, and Delta Lake. Delivered ETL/ELT pipelines processing millions of records daily for Fortune 500 clients across consumer goods and retail — owning architecture, performance tuning, data quality, and CI/CD deployment end-to-end. Comfortable across the full pipeline lifecycle: modelling a Star Schema, writing incremental DBT models, optimizing a Spark job, triaging a production incident, or reviewing a colleague's PR. Focused on pipelines that are maintainable by the next engineer, not just correct today.
الخبرة
Consultant – Data Engineering
<p>Engineered 10+ production ETL/ELT pipelines on Azure Databricks and ADF ingesting 20M+ records daily across supply chain, sales, and logistics domains — owning design, deployment, on-call support, and incident resolution.</p><p> Delivered a Bronze-Silver-Gold Medallion Architecture on Delta Lake with schema enforcement, structured checkpointing at each layer, and automated OPTIMIZE and Z-Ordering compaction jobs — providing the project its first auditable, lineage-tracked data tier.</p><p> Reduced Spark cluster compute time on recurring jobs by replacing sort-merge joins with broadcast joins, enabling Adaptive Query Execution (AQE), and applying partition pruning — changes validated by comparing Spark UI job timelines before and after.</p><p> Refactored a legacy Scala transformation codebase to Python/PySpark, cutting total lines by ~35%, centralizing exception handling and structured logging, and reducing new-engineer ramp-up from weeks to days.</p><p> Defined Star Schema dimensional models (Fact and Dimension tables) that the programmer adopted as the standard template across two additional workstreams — reducing modelling rework on each new domain.</p><p> Automated CI/CD release pipelines in Azure DevOps — replacing a manual promotion process and enabling the 3-engineer team to ship environment-validated changes to production with a complete audit trail.</p><p> Delivered config-driven Python ingestion frameworks for REST APIs, ERP flat files, and partner feeds — with centralized exception handling, retry logic on transient failures, and structured logging consumed by Azure Monitor.</p><p> Established production runbooks and monitoring hooks for all pipeline failures — enabling root-cause analysis on recurring incidents and cutting mean time to resolution across the team.</p>
Program Analyst – Data Engineering
<p>Owned end-to-end ELT pipeline development on Snowflake and DBT for Nike's global retail data warehouse — covering product catalogue, orders, inventory, and digital analytics across 6 upstream source systems.</p><p> Refactored full-refresh pipeline runs into incremental DBT models backed by Snowflake Streams and Tasks with SCD Type 2 logic — eliminating the morning reporting delay that had been a recurring business complaint.</p><p> Orchestrated legacy data migration into Snowflake using Time Travel for point-in-time validation and Zero Copy Clone for environment promotion — completing cutover with zero data loss and no business downtime.</p><p> Engineered a Snowpipe-driven, event-triggered ingestion layer for flat file and API feeds, reducing data latency from source arrival to analytics availability from hours to under 15 minutes.</p><p> Improved analytical query performance on high-volume Fact tables by applying Snowflake Clustering Keys with micro-partitioning analysis, and rewriting CTE chains using Window Functions and Stored Procedures.</p><p> Standardized DBT testing (not-null, uniqueness, referential integrity) and Jinja macro patterns across 15 data models — establishing the project's first systematic data quality framework and reducing analyst-reported data issues.</p><p> Reduced Snowflake credit consumption by tuning warehouse auto-suspend thresholds and restructuring Task scheduling to avoid overlapping compute — a cost optimization that required no changes to downstream queries.</p><p> Led code reviews, maintained Git branching conventions, and authored internal documentation on DBT patterns and Snowflake best practices — raising baseline code quality across the delivery team.</p>