Senior Data Engineer

What You'll Do

• Design and build batch and streaming data pipelines to support automated data ingestion, ML feature engineering and analytics across multiple product domains.
• Own end-to-end delivery of complex, ambiguous data initiatives, including architecture, implementation, testing, deployment, monitoring, and documentation.
• Develop and evolve the data platform to support large-scale data processing using modern cloud-native technologies.
• Automate data operations (validation, quality checks, alerting, backfills, and recovery workflows) to reduce manual effort and improve consistency.
• Optimize cost, performance, and reliability of data workloads.
• Partner closely with cross-functional teams (Data Science, Product, Engineering) to understand requirements, translate them into technical solutions.
• Evaluate and adopt new technologies (new processing engines, storage formats, orchestration tools, GenAI-assisted ingestion) to keep the platform modern and efficient.

What You Bring

• 5+ years of hands-on data engineering experience, building and maintaining production-grade data platforms and pipelines.
• Strong programming skills in general-purpose language (such as Python or Scala) for data processing, and SQL for data analytics.
• Deep experience with distributed data processing frameworks, such as Apache Spark, including performance tuning and optimization.
• Proven experience building data solutions using services on AWS (EMR, Lambda, S3, etc).
• Strong understanding of data modeling and data warehousing concepts, including partitioning, schema design for large-scale datasets.
• Experience operating and supporting production pipelines, including monitoring, alerting, incident response, and improving reliability over time.
• Solid foundation in software engineering practices, including version control, CI/CD, testing strategies, and code review.
• Strong communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders.

Preferred Qualifications

• Experience with streaming or near-real-time data processing (Kafka, Kinesis, etc).
• Hands-on experience with data orchestration tools (Airflow, Step Functions, etc).
• Familiarity with modern data platform patterns such as Data Lakehouse, Data Mesh, and large-scale data sharing across teams.
• Experience with prompt engineering using modern GenAI, Large Language Models (LLM).
• Experience mentoring other engineers and contributing to engineering-wide standards, best practices.