Director - Business Intelligence

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.

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 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.

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.

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