Job Description
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 solid 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 the surrounding ecosystem (Azure, lightweight ETL / data-prep tooling such as Trifacta or dbt) is a strong plus. AI-Assisted Data Development
• Hands-on experience developing data solutions with AI tooling — Databricks Genie, GitHub / Databricks Copilot, Claude Code, or comparable AI coding agents.
• Able to use AI assistants to accelerate pipeline build, transformation logic, and debugging while maintaining correctness, governance, and quality.
• A builder's mindset: comfortable pairing with AI tools as a force multiplier for the team, not a novelty. Media & Marketing Data Fluency You must understand not just how to engineer the data, but what the data means.
• Strong command of media and marketing data: what impressions, spend, clicks, and conversions represent, and how they relate.
• Understanding of how reach and frequency work — served vs. viewable impressions, de-duplicated reach, and frequency capping — and the implications for how data must be modeled.
• Familiarity with conversion tracking and attribution mechanics — pixels, tags,



post-click vs. post-view, and lookback windows.
• Knowledge of how media data connects across campaigns, placements, creatives, and channels, and how spend flows through to outcomes.
• Practical experience with media taxonomies and naming conventions as the foundation of trustworthy, joinable data. Leadership
• Proven experience leading and developing an engineering team, ideally in an offshore / DGS or global delivery model.
• Ability to set architecture and code standards, review work critically, and raise the quality bar across a team.
• Strong communication across time zones with onshore leads and stakeholders. Preferred / Nice to Have
• Experience with streaming / near-real-time ingestion patterns.
• Familiarity with trafficking / ad ops platforms (CM360, Prisma, DV360, TTD) and how their data lands in the platform.
• Exposure to semantic / metrics layers and how engineering choices affect downstream reporting (Power BI, dbt metrics).
• Experience in an agency, ad tech, or marketing analytics environment.
• CI/CD, testing, and DataOps practices for data pipelines. Qualifications
• 12+ years in data engineering, with several years in a leadership or director-level capacity.
• Demonstrated deep Databricks / Lakehouse expertise in production.
• Demonstrated domain knowledge of media and/or marketing data (hard requirement).
• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

📌 Director Business Intelligence (Bengaluru)
🏢 Dentsu Global Services
📍 Bengaluru