Shape the Future of Data - Join our mission to build the foundational pipelines and tools that power measurement, insights, and decision-making across our product, analytics, and leadership teams.
Develop the Platform Infrastructure - Build the core infrastructure that powers our data ecosystem including the Kafka events-system, DDL management with Terraform, internal data APIs on top of Databricks, and custom admin tools (e.g. Django-based interfaces).
Build Real-time Analytical Applications - Develop internal web applications to provide real-time visibility into platform behavior, operational metrics, and business KPIs integrating data engineering with user-facing insights.
Solve Meaningful Problems with the Right Tools - Tackle complex data challenges using modern technologies such as Spark, Kafka, Databricks, AWS, Airflow, and Python. Think creatively to make the hard things simple.
Own It End-to-End - Design, build, and scale our high-quality data platform by developing reliable and efficient data pipelines. Take ownership from concept to production and long-term maintenance.
Collaborate Cross-Functionally - Partner closely with backend engineers, data analysts, and data scientists to drive initiatives from both a platform and business perspective. Help translate ideas into robust data solutions.
Optimize for Analytics and Action - Design and deliver datasets in the right shape, location, and format to maximize usability and impact - whether thats through lakehouse tables, real-time streams, or analytics-optimized storage.
You will report to the Data Engineering Team Lead and help shape a culture of technical excellence, ownership, and impact.
Requirements: 5+ years of hands-on experience as a Data Engineer, building and operating production-grade data systems.
3+ years of experience with Spark, SQL, Python, and orchestration tools like Airflow (or similar).
Degree in Computer Science, Engineering, or a related quantitative field.
Proven track record in designing and implementing high-scale ETL pipelines and real-time or batch data workflows.
Deep understanding of data lakehouse and warehouse architectures, dimensional modeling, and performance optimization.
Strong analytical thinking, debugging, and problem-solving skills in complex environments.
Familiarity with infrastructure as code, CI/CD pipelines, and building data-oriented microservices or APIs.
Enthusiasm for AI-driven developer tools such as Cursor.AI or GitHub Copilot.
This position is open to all candidates.