Data Engineering Director

Most days tend to involve a blend of engineering oversight, architecture decisions, and cross-functional coordination with product, analytics, ML, and business teams. You'll often spend part of the time on strategic priorities — platform direction, vendor strategy, AI/ML enablement — and part on the operational fabric of pipeline reliability, data quality, and incident response.

The hardest part is often balancing the speed product and analytics teams want against the platform discipline that makes the data trustworthy long-term. You'll typically defend foundational investment in tooling, observability, and standards under pressure to deliver more dashboards and features, and you'll absorb the visibility of data quality incidents.

People who tend to thrive here are technically rigorous, architecturally minded, and skilled at influencing across product and analytics audiences. The trade-off is the always-on nature of data infrastructure and the cumulative weight of being responsible for pipelines that the whole company depends on. If you find satisfaction in building data foundations that make everything else possible, this role can be a strong destination in technology leadership.