Director - Data Engineering

Job Description:

Director, Data Engineering

Team: Data Experience (DEx) — Data Engineering

Reports to: Practice Lead / Group Lead, Data Engineering

Level: Director (DGS)

Location: Offshore (DGS, India)

Employment: Dentsu Global Services

The Data Experience (DEx) practice builds the data foundations, pipelines, and products that power Dentsu's media and marketing effectiveness work across Carat, iProspect, and Dentsu X. We are seeking a Director, Data Engineering to lead our offshore (DGS) engineering capability — owning the architecture, quality, and delivery of the data platform that everything downstream depends on.

This is a senior leadership and hands-on engineering role. You will set the standard for how DEx ingests, models, and serves media and marketing data on Databricks, and you will do it as a modern practitioner — using AI-assisted data development (Databricks Genie, Copilot, Claude Code) to move faster and raise quality. You will lead and mentor a team of DGS engineers while staying close enough to the code to set the bar yourself. This role demands deep Databricks expertise and a genuine understanding of what media data means, not just how to move it.

• Lead the offshore (DGS) Data Engineering team — setting architecture and delivery standards, reviewing code and design, mentoring engineers, and owning quality across the portfolio.
• 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.

• 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 the surrounding ecosystem (Azure, lightweight ETL / data-prep tooling such as Trifacta or dbt) is a strong plus.

• 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.

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.

• 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.

• 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.

• 10+ 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.

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