Senior Data Engineer
Data Engineering • Full-time, Onsite • Bangalore / Chennai / Kochi (Work from Office)
Exp: 4-6 years experience
About the Role
We are looking for a Senior Data Engineer to design, build, and operate the data pipelines and lakehouse platform that power analytics and data products across the business. You will own end-to-end ingestion, transformation, and modeling on Databricks and Microsoft Fabric - turning raw, heterogeneous source data into reliable, well-governed, analytics-ready datasets.
This is a hands-on senior individual-contributor role. Beyond writing production-grade pipelines, you will set engineering standards, review designs, and mentor mid-level engineers on the team. The role is industry-agnostic — you will work across a range of data domains and source systems.
What You'll Do
Build & operate pipelines - Design and maintain scalable batch and streaming data pipelines (ELT/ETL) on Databricks, ingesting from databases, files, APIs, and event streams.
Lakehouse & modeling - Architect and implement a medallion (bronze/silver/gold) lakehouse on Delta Lake; design dimensional and analytics-ready data models for downstream consumption.
Microsoft Fabric delivery - Deliver data into and across Microsoft Fabric (Lakehouse, Warehouse, OneLake, Pipelines/Dataflows) and enable BI and semantic-model consumption.
Orchestration & reliability - Orchestrate workflows with Airflow and/or Azure Data Factory; build in data-quality checks, monitoring, alerting, and SLAs.
Governance - Implement cataloging, lineage, and access controls using Unity Catalog and Microsoft Purview.
Performance & cost - Tune Spark jobs, storage layout, and cluster configuration for performance and cost efficiency.
Standards & mentorship - Define engineering best practices, conduct design and code reviews, and mentor mid-level data engineers.

What We're Looking For
4–6 years of qualified data engineering experience building and operating production data pipelines.




Strong hands-on experience with Databricks - Spark (PySpark/Spark SQL), Delta Lake, notebooks/jobs, and cluster management.
Proven experience designing and building data pipelines (batch and streaming) at scale, with a solid grasp of ELT/ETL patterns.
Experience with Microsoft Fabric - Lakehouse, Warehouse, OneLake, and Fabric Pipelines/Dataflows.
Strong data modeling skills - medallion/lakehouse architecture and dimensional modeling.
Proficiency in Python and advanced SQL .
Experience with workflow orchestration ( Airflow and/or Azure Data Factory ).
Experience with data governance tooling ( Unity Catalog and/or Microsoft Purview ).
Experience with streaming ingestion ( Kafka / Event Hubs and Spark Structured Streaming).
Ability to work independently, set technical direction, and mentor others.

Nice to Have
AWS data experience - S3, Glue, Redshift, EMR, Athena, or Lake Formation (a strong plus).

Infrastructure-as-code and CI/CD for data (Terraform, Git-based deployment, automated testing).
Experience with dbt for transformation and testing.
Familiarity with Power BI semantic models and BI enablement.
Databricks or Microsoft (Azure) certifications.

Technology Stack
Databricks: Apache Spark (PySpark, Spark SQL), Delta Lake, Databricks Workflows/Jobs, Unity Catalog, Delta Live Tables.
Microsoft / Fabric: Microsoft Fabric (Lakehouse, Warehouse, OneLake, Pipelines, Dataflows Gen2), Azure Data Factory, Azure Data Lake Storage, Microsoft Purview, Power BI.
Pipelines & Orchestration: Apache Airflow, Azure Data Factory, Kafka / Azure Event Hubs, Spark Structured Streaming.
Languages & Tooling: Python, SQL, Git, CI/CD, Terraform, dbt.
AWS (plus): S3, AWS Glue, Amazon Redshift, EMR, Athena, Lake Formation.
Why Join Us
You'll take real ownership of a modern lakehouse platform, work across a broad set of data domains, and shape how the team builds data. We value skills and impact over formal credentials — there is no degree or certification requirement for this role.

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