Data Engineer II

Overview

Design, build and scale the data platform that powers analytics, product insights and data-driven decision making, using modern data technologies.

What you'll do

• Design, build and maintain scalable batch and real-time data pipelines using Maxwell, Kafka, Spark and dbt to power analytics and business-critical applications.
• Develop and optimize data models following Medallion Architecture (Bronze, Silver, Gold) to create reliable, reusable and high-quality datasets.
• Build and maintain cloud-native data platforms using S3, Spark, Trino and BigQuery, ensuring scalability, reliability and cost efficiency.
• Design robust data ingestion frameworks leveraging CDC (Maxwell), Kafka and event-driven architectures to support near real-time data processing.
• Create, optimize and maintain data warehouses and data marts that enable fast, reliable reporting and self-service analytics.
• Partner closely with Product Managers, Data Analysts, Backend Engineers and Business stakeholders to translate business requirements into scalable data solutions.
• Develop reusable dbt models, testing frameworks and documentation to improve data quality, governance and developer productivity.
• Optimize Spark jobs, Trino queries and storage layouts for performance, reliability and cost efficiency.
• Own the end-to-end lifecycle of critical data pipelines, ensuring high availability, monitoring, SLA adherence and proactive incident resolution.
• Build and enhance the core data platform by developing reusable frameworks, automation, CI/CD pipelines and engineering best practices.
• Ensure data quality through validation, monitoring, lineage and observability while implementing best practices for security and governance.
• Enable analytics teams by delivering trusted datasets, semantic models and dashboards that power decision-making through Metabase.

What you'll need

• 4+ years of hands-on experience designing and building scalable data platforms, data lakes and data warehouses.
• Strong proficiency in Spark (Scala, Python) and SQL, with experience building production-grade data pipelines and distributed data processing applications.
• Hands-on experience with Apache Spark and a solid understanding of distributed data processing, performance tuning and optimization.
• Experience building batch and streaming data pipelines using technologies such as Kafka, CDC/Maxwell or similar event-driven architectures.
• Strong understanding of modern data lake architectures, including Medallion Architecture, data modeling, partitioning and storage optimization.
• Experience working with cloud-native data platforms and technologies such as Amazon S3, BigQuery, Trino or similar analytics engines.
• Solid experience designing dimensional models, star schemas and building reliable data marts that support analytics and business intelligence.
• Hands-on experience with dbt, including developing reusable models, implementing automated testing and maintaining documentation.
• Strong knowledge of data quality, observability, lineage and engineering best practices to build reliable and maintainable data products.
• Experience optimizing large-scale data pipelines, SQL queries and distributed processing jobs for performance, scalability and cost efficiency.
• Familiarity with CI/CD, Git-based development workflows, infrastructure automation and modern software engineering best practices.
• Excellent problem-solving skills with the ability to independently own projects from design through production.
• Strong communication and stakeholder management skills, with experience collaborating across Product, Engineering, Analytics and Business teams.
• A passion for building scalable data platforms and continuously improving developer experience, platform reliability and operational excellence.

Details

• Remote environment offered.