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Product Data Engineering & Analytics Director
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Product Data Engineering & Analytics Director
Product Data Engineering & Analytics Director is required by a global software company to lead the design and delivery of data systems that capture and analyse product usage across a portfolio of software products.
Key responsibilities:
- Defining and implementing the data architecture for product telemetry across all products.
- Owning the design and evolution of the event data pipeline, from instrumentation through ingestion, transformation, and storage in a lakehouse environment.
- Establishing and enforcing standards for event instrumentation (schemas, naming, versioning, required attributes).
- Designing and building reusable data assets, including curated datasets, semantic layers, and governed metrics.
- Defining consistent data models for usage, engagement, and outcome measurement across products.
- Developing and maintaining analytics and reporting capabilities for product teams.
- Evaluating and selecting product analytics tools based on integration with the data platform, governance constraints, and cost.
- Overseeing data ingestion pipelines and processing workflows to ensure reliability, data quality, and traceability.
- Defining measurement frameworks and success metrics for product features and initiatives.
- Building dashboards and analytical outputs used by product, engineering, and leadership teams.
- Leading and growing a team of data engineers and product analysts.
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Required experience and skills:
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- Strong background in data engineering and large-scale data platform design.
- Experience implementing event-based telemetry systems and instrumentation standards.
- Experience defining product metrics, KPIs, and measurement frameworks.
- Hands-on experience with Databricks (MLflow, Unity Catalog, or Feature Store is a plus).
- 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 is beneficial.
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