Director - Business Intelligence

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.