Job OverviewWe are looking for a Senior Data Engineer based in the US to design, build, and maintain robust data pipelines and scalable infrastructure across our cloud platform. In this role, you will play a key part in enabling Data Products throughout their lifecycle-sourcing data via batch and stream processing, optimizing data architecture, and ensuring data quality. You will collaborate closely with Data Scientists and ML Engineers to automate workflows and deploy resilient data pipelines in production.
- Work Location / Employment: US-based (Remote / Hybrid) - Must be authorized to work in the United States.
Tech Stack & Key Technologies- Cloud Platforms: AWS (Redshift, Glue, EMR, MWAA, S3) & GCP (BigQuery, Cloud Storage)
- Languages & Frameworks: Python, PySpark, SQL (advanced optimization & tuning)
- Data Engineering & Ops: Batch & Stream Processing, ETL/ELT Pipelines, Data Modeling, Data Warehousing, Orchestration
- DevOps, Infrastructure & Tools: Docker, Terraform, CI/CD pipelines, UNIX/Linux environments, MLOps best practices
Key Responsibilities- Pipeline Architecture & Development: Design, build, and deploy scalable, decoupled, and high-performance data pipelines for batch and streaming data ingestion into Data Lakes and Data Warehouses.
- ETL/ELT Processing: Develop and optimize efficient data pipelines using Python, PySpark, and advanced SQL.
- Collaboration with ML & Data Science: Partner with Data Scientists and ML Engineers to build resilient pipelines, automate MLOps workflows, and gather model interaction data to support retraining loops.
- Performance Optimization & Monitoring: Continuously monitor, troubleshoot, and optimize pipeline execution, query performance, and storage resource utilization across cloud platforms.
- Data Quality & Governance: Implement data quality frameworks and testing controls to ensure data integrity, reliability, and governance across raw and curated datasets.
- DevOps & Standards: Apply MLOps and DevOps best practices, maintain CI/CD pipelines, use Docker for containerization, and document technical standards and architectures.
Qualifications & Requirements- Experience: 5+ years of experience in Data Engineering, with a proven track record of designing and running large-scale data systems.
- Core Languages: Mastery of Python and advanced SQL (query optimization and performance tuning).
- Cloud & Big Data Tools: Hands-on experience with AWS (Redshift, Glue, EMR, MWAA, S3) or GCP (BigQuery) and modern data processing engines (PySpark).
- Engineering Ecosystem: Proficiency with UNIX environments, Docker, Git, and CI/CD pipelines.
- Data & ML Lifecycle: Solid understanding of data structures, batch/streaming processing, and familiarity with MLOps or the ML deployment lifecycle.
- Communication: Excellent verbal and written English communication skills.
Nice to Have- Experience with Infrastructure as Code (e.g., Terraform).
- Experience building and maintaining scalable REST APIs.
- Exposure to prompt engineering workflows or generative AI data pipelines.
