Director of Product Data Engineering & Analytics is required by a global software company 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
Required experience and skills
- 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