Data Engineer

In This Role

As a Data Engineer on the Data Pipeline team, you will build and support modern, large-scale analytics platforms that power data-driven decision-making across the studio. You’ll work hands-on with cloud-based lakehouse and warehouse architectures, designing and maintaining real-time and batch data pipelines that serve business, design, test, and development stakeholders. This role sits at the intersection of data engineering, analytics enablement, and documentation, helping teams not only use data effectively but also understand and trust the systems behind it.

You’ll contribute to a culture of test-driven, data-informed development by shaping how data is captured, processed, documented, and shared. Attention to detail, strong engineering fundamentals, and a passion for scalable, modern data architecture are key to success in this role.

• Design, build, and maintain scalable, high-quality data pipelines supporting both real-time and batch analytics workloads
• Develop and optimize ETL/ELT processes using modern cloud data technologies and orchestration tools
• Contribute to the design and evolution of lakehouse and warehouse data models that support analytics and reporting needs
• Partner with business, design, test, and development teams to understand data requirements and improve data capture strategies
• Help lead technical writing initiatives, including goal-setting, planning, and execution
• Write, edit, and maintain clear, concise, and well-structured technical documentation
• Create and improve documentation standards, organization, and knowledge-sharing processes
• Organize and curate documentation to ensure it is intuitive, discoverable, and up to date
• Advocate for consistent knowledge sharing across teams and studios
• Research and propose improvements to documentation workflows and solutions to existing pain points
• Apply modern engineering best practices to ensure reliability, scalability, and data quality across platforms

• 5+ years of professional experience working with SQL
• 5+ years of experience designing and implementing scalable ETL processes, including data movement and data quality tooling
• Hands-on experience with cloud-based data orchestration solutions (e.g., Azure Data Factory or equivalent)
• 3+ years of experience with modern big data analytics platforms, including:
  • Data lakes
  • Distributed processing frameworks (e.g., Spark)
  • Columnar storage formats such as Parquet
• 2+ years of experience building and supporting cloud-hosted data systems
• Strong understanding of data modeling, pipeline reliability, and performance optimization

• Experience building data pipelines using cloud-native analytics platforms and services (e.g., data factory tools, analytics warehouses, and Spark-based processing)
• Hands-on experience working with Delta Lake and transactional data lake formats
• Experience querying and integrating data from high-performance analytics engines (e.g., time-series or log-based systems such as Kusto/Azure Data Explorer)
• Exposure to AI/ML-focused data engineering use cases, including:
  • Feature engineering and feature stores
  • Model training and serving datasets
  • Model monitoring and observability pipelines
• Experience preparing, governing, and securing datasets for modern AI applications, including:
  • LLM and RAG workflows
  • Experimentation and A/B testing
  • Privacy-aware and compliant data access patterns