Databricks Azure -DataEngineer & Sr.Data Engineer

Tiger Analytics • MLOps • AI Consulting & Integration • Python • SQL • Azure Data Factory • 4 days ago
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Salary
≈ $20k – $40k per year (Estimated)
Location
In office (Chennai)
Seniority
Senior

Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Sep 23, 2026.

Overview
Company
Impact
Profile match

Tiger Analytics

Tiger Analytics is an advanced analytics and artificial intelligence consultancy founded in 2011 that builds data science and decision systems for consumer goods, retail, insurance, healthcare and technology clients. Its engagements typically run from data platform construction through model development to deployment and monitoring in production, rather than stopping at proof of concept, and it also packages recurring problem shapes into reusable accelerators. Headquartered in Santa Clara with its largest delivery organisation in India, it employs several thousand data scientists and engineers across North America, Europe, India and Australia.
tigeranalytics.com • HQ: Santa Clara, United States • MLOps • Data Engineering & Migration Services • AI Consulting & Integration • Analytics & BI Consulting • 1001-5000 employees • Est. 2011
Tech stack of Tiger Analytics

  • Design, build, and maintain robust cloud data pipelines using Azure Data Factory (ADF), Azure Databricks, PySpark, and Spark SQL.

  • Implement and manage the Medallion Architecture - moving and transforming data through Bronze (raw/audit), Silver (cleansing/dedup/SCD), and Gold (business aggregates) layers.

  • Perform complex data transformations, cleansing, deduplication, and incremental loads using Delta MERGE, supporting both Slowly Changing Dimensions (SCD Type 1 and Type 2).

  • Optimize Spark workloads by tuning shuffle partitions, managing memory to prevent OOM errors, and leveraging Adaptive Query Execution (AQE).

  • Apply optimized join strategies, including broadcast joins for small datasets and salting techniques to handle data skew.

  • Implement robust exception handling, file dependency validation, and asynchronous batch processing to ensure pipeline reliability.

  • Ensure high data quality through schema enforcement, schema evolution handling, and validation against expected target criteria.

  • Monitor, troubleshoot, and resolve production job failures by analysing cluster scaling behaviour, Spark UI metrics, and physical query plans.

  • Build and maintain CI/CD pipelines for Databricks using Git, Azure DevOps, and Databricks Asset Bundles (DABs), with environment-specific parameterization for Dev, Test, and Prod.
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