About the service
I build industrial data platforms on Databricks and drive projects from the first line of code to delivery so your analysts and data scientists can trust every table. My specialty is lakehouse: I design Spark pipelines that pull data from databases, APIs, files, and streaming sources, then organize it into Delta tables — versioned, transactionally safe, and fast to query. I work by medallion architecture, moving data through bronze, silver, and gold layers, so cleaning, deduplication, and business logic each live in their own clear and testable stage rather than one tangled script you're afraid to touch.
I typically cover the entire platform lifecycle. I configure workspaces, clusters, and job compute with sensible autoscaling so you don't pay for idle time, and orchestrate everything through Databricks Workflows or your existing scheduler — with retries, alerts, and data quality checks. On the governance side I bring in Unity Catalog: a single catalog for tables, volumes, and models, row and column-level access control, data lineage and audit, so security and compliance are baked in from the start, not bolted on later. I separately handle performance: partitioning, Z-order, liquid clustering, and file compaction keep both cost and query latency in check.
The result is a maintained foundation, not a one-off demo. I write modular, tested code in PySpark and SQL, document the model and run schedules, hand over pipelines your team can grow on their own. Whether you're migrating legacy storage, standing up your first lakehouse, or preparing clean feature tables for machine learning — I bring pragmatism to ship reliable solutions on time.
— Spark and Delta pipelines following the bronze/silver/gold scheme
— Workflows orchestration, CI/CD, and data quality checks
— Governance through Unity Catalog: lineage and access control
— Performance tuning and cost optimization for clusters and queries
I typically cover the entire platform lifecycle. I configure workspaces, clusters, and job compute with sensible autoscaling so you don't pay for idle time, and orchestrate everything through Databricks Workflows or your existing scheduler — with retries, alerts, and data quality checks. On the governance side I bring in Unity Catalog: a single catalog for tables, volumes, and models, row and column-level access control, data lineage and audit, so security and compliance are baked in from the start, not bolted on later. I separately handle performance: partitioning, Z-order, liquid clustering, and file compaction keep both cost and query latency in check.
The result is a maintained foundation, not a one-off demo. I write modular, tested code in PySpark and SQL, document the model and run schedules, hand over pipelines your team can grow on their own. Whether you're migrating legacy storage, standing up your first lakehouse, or preparing clean feature tables for machine learning — I bring pragmatism to ship reliable solutions on time.
— Spark and Delta pipelines following the bronze/silver/gold scheme
— Workflows orchestration, CI/CD, and data quality checks
— Governance through Unity Catalog: lineage and access control
— Performance tuning and cost optimization for clusters and queries
