Are you passionate about big data and eager to work with cutting-edge technologies? We have an exciting opportunity for a Data Engineer - Spark Developer to join our dynamic and expanding development teams. Whether you prefer to work from our vibrant offices in Athens or remotely, we welcome your talent and enthusiasm. What You'll Do: - Design, develop, test, deploy, maintain, and improve data pipelines; - Coding using Apache Spark on Azure Databricks; - Design and develop big data architectures using Azure Data Factory, Service Bus, BI, Databricks, and other Azure Services.

Are you ready to take your career to the next level? Join our expanding development teams in Athens, or work remotely as a Data Engineer. As part of our agile team, you'll play a key role in designing and implementing big data solutions on a scalable cloud platform, analyzing millions of real-life and real-time data points to derive advanced insights and enhance analytics capabilities for end users. What You'll Do: - Design and implement batch processing pipelines using Spark (Python or Scala) and SQL; - Design and Implement streaming ETL/ELT processes from a diverse set of data sources; - Write code supporting the development of big data solutions, implementing complete data integration or analytic use cases; - Design and implement APIs in any modern Python API framework; - Communicate effectively with our Business Analysis teams to ensure alignment with business requirements; - Write representative end-to-end and functional tests using open-source tools; - Implement monitoring solutions for our data platform through alerts and dashboards.

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.