Data Engineer - Databricks Focus

Allison Transmission; Pune District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Allison Transmission is seeking an experienced Data Engineer to join the data team and modernize analytics infrastructure. The role focuses on migrating and rehydrating ~500 Power BI reports into a new Lakehouse environment while improving data sources and performance.

You will design scalable data pipelines with Databricks, lead migration efforts, and implement CI/CD for data assets. Collaboration with analysts and BI teams is essential to ensure reliable data delivery.

Qualifications

• 4+ years of hands-on data engineering experience.
• Strong proficiency in Python and SQL.
• Deep experience with Databricks (Delta Lake, Unity Catalog, workflows).
• Proven track record implementing CI/CD for data workloads using Databricks Asset Bundles and GitHub Actions.

Responsibilities

• Design, build, and maintain scalable data pipelines using Databricks (Delta Lake, Unity Catalog, Spark).
• Lead or contribute to migration and rehydration of ~500 Power BI reports in 2026, including optimizing data sources.
• Implement and maintain CI/CD pipelines for data assets using Databricks Asset Bundles and GitHub Actions.
• Collaborate with data analysts, BI developers, and stakeholders to ensure data availability, performance, and reliability.
• Optimize ETL/ELT processes for performance, cost, and maintainability.
• Establish best practices for version control, testing, and deployment of notebooks, workflows, and Delta Live Tables.

Skills

Python

SQL

Databricks

CI/CD practices

Spark

Cloud platforms

Certifications

Tools

Databricks

GitHub Actions

Delta Lake

Unity Catalog

Spark SQL

Job description

We are seeking an experienced Data Engineer to join our growing data team and play a key role in modernizing our analytics platform. In 2026, we will be executing a large-scale migration and rehydration of ~500 existing Power BI reports, including re-connecting and optimizing data sources in a new Lakehouse environment.

Key Responsibilities

• Design, build, and maintain scalable data pipelines using Databricks (Delta Lake, Unity Catalog, Spark).
• Lead or significantly contribute to the migration and rehydration of approximately 500 Power BI reports in 2026, including re-pointing and optimizing data sources.
• Implement and maintain CI/CD pipelines for data assets using Databricks Asset Bundles (DAB), GitHub Actions, and other modern DevOps practices.
• Collaborate with data analysts, BI developers, and business stakeholders to ensure data availability, performance, and reliability.
• Optimize ETL/ELT processes for performance, cost, and maintainability.
• Establish best practices for version control, testing, and deployment of notebooks, workflows, and Delta Live Tables.

Required Experience & Skills

• 4+ years of hands-on data engineering experience (5-7+ years of experience overall).
• Strong proficiency in Python and SQL.
• Deep experience with Databricks (workspace administration, cluster management, Delta Lake, Unity Catalog, workflows, and notebooks).
• Proven track record implementing CI/CD for data workloads (preferably using Databricks Asset Bundles and GitHub Actions).
• Solid understanding of Spark (PySpark and/or Spark SQL).
• Experience with infrastructure-as-code and modern data DevOps practices.
• Relevant certifications strongly preferred:
• Databricks Certified Data Engineer Associate or Professional
• Azure Data Engineer Associate (DP-203) or equivalent AWS/GCP certifications

Nice-to-Have / Bonus Skills

• Experience extracting data from SAP/HANA or S/4HANA systems (via ODP, CDS views, SDA, etc.).
• Previous large-scale Power BI migration or re-platforming projects.
• Familiarity with Databricks SQL warehouses, Serverless, or Lakehouse Monitoring.
• Experience with dbt, Delta Live Tables, or Lakeflow.

If you have strong Databricks + CI/CD + Asset Bundles experience and are excited about transforming a large Power BI footprint into a modern Lakehouse architecture, we’d love to talk!