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Product Data Engineering & Analytics Director

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Summary

Director role at a global software company owning product telemetry end to end: designing event instrumentation standards, pipelines into a Databricks lakehouse, governed metrics and dashboards, while leading and mentoring a team of data engineers and product analysts. Core stack includes Databricks, SQL and Python.

Product Data Engineering & Analytics Director required by a global software company to lead the design and delivery of data systems that capture and analyse product usage across its software portfolio. You’ll own the telemetry architecture end to end, from instrumentation standards through to governed metrics and dashboards, and lead a team of data engineers and product analysts.

Key responsibilities:

  • Define and implement the data architecture for product telemetry across all products
  • Own the event data pipeline, from instrumentation through ingestion and transformation to storage in a lakehouse environment
  • Set and enforce standards for event instrumentation, including schemas, naming, versioning and required attributes
  • Build reusable data assets: curated datasets, semantic layers and governed metrics
  • Define measurement frameworks, data models and success metrics for usage, engagement and outcomes
  • Deliver analytics, reporting and dashboards for product, engineering and leadership teams
  • Evaluate and select product analytics tools, balancing platform integration, governance and cost
  • Lead, mentor and grow a team of data engineers and product analysts

Required experience and skills

  • Strong background in data engineering and large-scale data platform design
  • Experience implementing event-based telemetry systems and instrumentation standards for software products
  • Experience defining product metrics, KPIs, and measurement frameworks
  • Hands-on experience with Databricks
  • Strong SQL and Python skills
  • Experience working across the full data lifecycle
  • Experience leading and mentoring engineering or analytics teams
  • Familiarity with product analytics tools and experimentation platforms
  • Experience applying machine learning techniques to usage or behavioural data will be beneficial


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See also

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