Note: This role is primarily remote, with the expectation to visit our Eindhoven office around 1–2 times per month for in-person collaboration. At GoodHabitz, we are building an activation-first product strategy that helps learners start, stick, and get value fast — across both LMS-integrated and native platform experiences. We are hiring a Product Data Lead to found and shape the product data discipline from the ground up. This role goes beyond analysis: you will design the event taxonomy, instrumentation standards, and modeled analytics foundations that make product decisions measurably defensible. You will partner closely with Product and Engineering to turn messy, fragmented data into a trusted system (funnels, cohorts, retention), and install a repeatable metrics ritual that enables leadership to steer activation → engagement → retention → GRR with clarity. As the discipline matures, you will help define how product data scales — through systems, processes, and potentially team expansion — based on demonstrated impact rather than pre-set headcount.  This role reports to the Director of Product and will have high visibility across product and executive leadership.  Key Responsibilities Product Data Foundations (Taxonomy + Instrumentation)  - Define and drive adoption of a product event taxonomy and naming conventions for the highest-leverage activation surfaces.  - Partner with engineering to implement and maintain a tracking plan, including clear ownership and change management.  - Establish instrumentation quality monitoring so broken or missing events are detected early.  - Create clear documentation so teams can use events consistently across products and regions.  Activation & Retention Measurement (Funnels + Cohorts)  - Build a working activation funnel that supports segmentation (LMS vs Platform, coach vs no coach, key cohorts), and is used in product reviews.  - Create D0/D7/D14 (and beyond) retention tracking for key cohorts with explicit cohort definitions and repeatable models.  - Translate product questions into robust analysis patterns, and teach teams how to self-serve.  Executive Narrative & Metrics Ritual  - Install a monthly or bi-weekly product metrics ritual with a small set of agreed metrics, definitions, and owners.  - Produce a clear “Activation → Engagement → Retention → GRR” measurement narrative that leadership can rely on.  - Surface risks and data integrity gaps early, and propose sequencing to resolve them.  Cross-Functional Partnership & Structural Enablement  - Clarify the working model between product data, analytics engineering, and domain teams (who builds what, and what gets prioritized).  - Define requirements for critical identifiers and align stakeholders on scope and sequencing.  - Lead the design and sequencing of critical identity plumbing (e.g., account-level joins across product and revenue systems) to enable reliable product → retention analysis.  - Balance speed with rigor: ship practical v1 models and dashboards, then iterate as the system matures.

• Design and implement the long-term data architecture using modern technologies and frameworks. • Build and maintain scalable ETL/ELT pipelines in Python, SQL, and dbt—ingesting data via APIs, web scraping, and streaming sources. • Develop and operate data pipelines using orchestration frameworks such as Temporal and Dagster. • Design data models and schemas for our cloud warehouse (Databricks) and relational databases; contribute to the development of our ML feature store. • Optimize workflows for performance and cost efficiency. • Drive large, cross-functional data initiatives from planning to execution. • Partner with AI and engineering teams to ensure high-quality datasets for machine learning and analytics. • Collaborate with product managers, scientists, and engineers to gather requirements and deliver robust data products. • Mentor other engineers in best practices for data ingestion, architecture, and scalable pipeline design. • Support the software testing cycle, debug code, and resolve issues found during QA or user acceptance testing.

• Design and oversee key forward- and reverse-ETL patterns to deliver data to relevant stakeholders. • Develop scalable patterns in the transformation layer to ensure repeatable integrations with BI tools across various business verticals. • Expand and maintain the Alpaca Data Lakehouse architecture's constantly evolving elements. • Collaborate closely with sales, marketing, product, and operations teams to address key data flow needs. • Operate the system and manage production issues in a timely manner.

• Design, build, and maintain scalable and reliable data pipelines to support analytics, ML models, and business reporting. • Collaborate with data scientists and analysts to ensure data is available, clean, and optimized for downstream use. • Implement data quality checks, monitoring, and validation processes. • Work with cross-functional teams to design efficient ETL/ELT workflows using modern data tools. • Integrate data from multiple sources (databases, APIs, third-party tools) into centralized storage solutions (data lakes/warehouses). • Support cloud-based infrastructure for data storage and retrieval. • Monitor, troubleshoot, and optimize existing data pipelines to handle large-scale, real-time data flows. • Implement best practices for query optimization and cost-efficient data storage. • Ensure data is available and accessible for business-critical operations. • Partner with product, engineering, and business stakeholders to understand data requirements. • Document data workflows, schemas, and best practices. • Support a culture of data reliability, governance, and security.