Senior Data Engineer

Senior Data Engineer

Location: 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

46 years of professional 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.