Staff Data Engineer
Apex Systems
Staff Data Engineer
Location: United States (Remote) Employment Type: Contract Role Overview As a Staff Data Engineer, you will design, implement, and optimize data pipelines for models that power player features. You will work with petabytes of data and advanced processing technologies to deliver scalable systems. This role focuses on building and maintaining high-quality datasets and pipelines that fuel recommendation systems, supporting data-driven personalization across various platforms. Key Responsibilities- Design, build, and maintain scalable data pipelines for structured and semi-structured data to support analytics, machine learning models, and player-facing systems.
- Implement efficient and reliable data models and transformations within the central data warehouse, focusing on Medallion architecture.
- Develop and productionize pipelines to ingest, transform, and serve data for various systems, including instrumentation for A/B testing and key performance metrics.
- Collaborate with Data Scientists, Machine Learning Engineers, and Software Engineers to ensure data quality and schema clarity.
- Diagnose and resolve issues in data pipelines, optimizing for reliability, performance, and cost efficiency.
- Apply privacy, security, and responsible data use guidelines when building or accessing behavioral datasets.
- Document data models, pipelines, data contracts, and SLAs to ensure transparency.
- Contribute to team engineering practices, including coding standards, testing strategies, and operational best practices.
- Participate in on-call rotations, perform code reviews, and support the mentorship of other engineers.
- Proficiency in big data tools and programming languages such as Python, Scala, Spark, and SQL.
- Hands-on experience with Databricks for building and operating scalable data pipelines.
- Experience with orchestration and workflow tools like Airflow, Dagster, or Prefect.
- Experience with DBT for modular data modeling and transformation.
- Familiarity with cloud-based data infrastructure, particularly AWS or GCP.
- Understanding of Medallion architecture, schema design, and data modeling principles.
- Exposure to streaming data pipelines or event-driven ingestion using technologies like Kafka, Kinesis, or Pub/Sub.
- Working knowledge of data quality, testing, version control, and observability best practices.
- Experience supporting personalization, recommendations, or player-facing ML systems.
- Exposure to feature stores, ML data pipelines, or online/offline data management patterns.
- Knowledge of event-based or contextual recommendation signals.
- Interest in contributing to data architecture and standards.
Vacancy posted 4 days ago
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