Role Summary • Own the Databricks Lakehouse end-to-end: ingestion, Medallion (Bronze/Silver/Gold) modeling, Unity Catalog governance, Lakeflow pipelines, and performance.

- Design and build robust, scalable pipelines that bring together media and marketing data from many platforms into governed, analytics-ready datasets.
- Drive adoption of AI-assisted data development across the team using Databricks Genie, Copilot, and Claude Code to accelerate pipeline build, transformation, and debugging without sacrificing rigor.
- Establish and enforce data taxonomies, schema standards, data quality checks, and a single source of truth for core media data.
- Partner with visualization, data science, and Ad Ops teams to source data correctly, validate it, and resolve discrepancies between platform truth and downstream reporting.
- Contribute to the productization of the data platform—moving from bespoke, client-by-client pipelines toward reusable, standardized, semi-automated data products.
- Manage capacity, utilization, and delivery health for the DGS engineering team, coordinating closely with onshore leadership on staffing and priorities.



Core Skills & Requirements Databricks & Data Engineering
- Deep, hands-on Databricks expertise: Spark, Delta Lake, Unity Catalog, Lakeflow/Delta Live Tables, Medallion architecture, and performance tuning.
- Expert-level SQL and strong Python for data engineering; production experience building and operating pipelines at scale.
- Solid grasp of data modeling, orchestration, data quality, and governance—building for reuse and maintainability, not one-off delivery.
- Experience with Azure and lightweight ETL/data-prep tools such as Trifacta or dbt is a robust plus.

AI-Assisted Data Development

- Hands-on experience developing data solutions with AI tooling such as Databricks Genie, GitHub/Databricks Copilot, Claude Code, or comparable AI coding assistants.
- Able to use AI assistants to accelerate pipeline development, transformation logic, and debugging while maintaining correctness, governance, and quality.
- A builder's mindset, using AI as a force multiplier rather than a novelty.

📌 Director - Data Engineering (Maharashtra)
🏢 Dentsu Global Services
📍 Maharashtra