04/15/2026 updated
100 % available
Senior Data Engineer
Bengaluru, India
Only remote
BE Computer Science and Data EngineeringAbout me
Most data teams struggle with pipelines they can't trust and infrastructure that wasn't built to scale. I fix that. With 7+ years across Snowflake, dbt, Airflow, Spark, Kafka, and Microsoft Fabric, I build production-grade data platforms on AWS, GCP, and Azure that are reliable, scalable, and clean.
Apache AirflowCouchDBData MigrationPython (Programming Language)PostgreSQLAzure Data FactorySnowflakedbtMicrosoft FabricGoogle BigqueryAzure Service FabricAirtableAmazon RedshiftDatabricks
Most data teams are held back by unreliable pipelines, warehouses they cannot trust, and data infrastructure that was never built to scale. That's exactly what I fix.
As a Senior Data Engineer, I don't just write SQL and call it a pipeline. I architect end-to-end data systems where reliable ingestion feeds into clean, versioned transformations that power decisions your business can act on. My approach prioritizes fault tolerance, scalability, and observability across both batch processing and real-time analytics workloads. This ensures your data infrastructure is not just functional, but resilient and audit-ready.
Whether you need cloud data migration, data platform modernization to a Modern Data Stack (Snowflake/dbt/Airflow, Microsoft Fabric), or streaming analytics infrastructure, I deliver production-grade systems that help technical founders and data teams eliminate pipeline debt, automate complex data workflows, and build scalable infrastructure ready for AI workloads.
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Where I make the biggest impact:
✅ I lead data migration and data platform modernization projects, replacing brittle ETL and ELT pipelines with a Modern Data Stack built on Snowflake, dbt, Airflow, and Microsoft Fabric.
✅ Every engagement includes Medallion Architecture design, full test coverage, CI/CD for data models, data lineage tracking, and documentation that outlasts the project.
✅ Batch and real-time pipelines that are idempotent, schema-drift tolerant, and monitored through data observability frameworks.
✅ Warehouse modeling: Star Schema, dimensional modeling, dbt projects, analytics engineering, and a metrics layer backed by a data catalog.
✅ Distributed streaming: Kafka, Flink, Spark Structured Streaming, exactly-once semantics, and latency guarantees.
✅ AI data pipelines feeding LLMs and ML systems with clean, structured data from ingestion through serving. ✅ Data governance through data mesh, catalog implementation, metadata management, and cross-system integration.
✅ Data quality enforced end to end with automated frameworks, SLA monitoring, and observability that catches bad data before stakeholders see it.
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What I Build With:
- Warehouses & Lakehouses: Snowflake, BigQuery, Redshift, Databricks, Fabric, Delta Lake, Iceberg
- Transformation: dbt (Core & Cloud), SQLMesh, Spark, PySpark
- Orchestration: Airflow, Dagster, Prefect, Azure Data Factory\
- Streaming: Kafka, Kinesis, Pub/Sub, Flink
- Ingestion: Fivetran, Airbyte, Stitch, Hevo, Meltano, CDC pipelines
- Cloud: AWS, GCP, Azure
- Languages: Python, SQL (Snowflake, BigQuery, T-SQL, PL/pgSQL)
- BI: Looker, Tableau, Power BI, Metabase, Superset
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If your data infrastructure needs to be faster, cleaner, and trustworthy, please send a message, and I'll take it from there.
Languages
EnglishNative speakerHindiNative speaker
Project history
Certificates
Google Analytics
Google2022
Certified Data Engineer
RapidMiner2020
Certified Developer
Dataiku2020