About The Role

Join our team as a Data Engineer, where you will design, build, and maintain high-throughput ETL/ELT pipelines for data ingestion, processing, and storage solutions. You will develop complex, performance-tuned data transformations using Python and high-performance SQL, and contribute to our technical strategy for scaling to support future business needs. You will also implement data ops best practices, drive data reliability and observability strategy, and work with distributed processing systems like Spark, Flink, or Kafka. Additionally, you will partner with the ML Platform team to prepare and provide clean, feature-rich datasets for model training and inference, and ensure data stewardship by contributing to documentation, discoverability, and implementing robust data privacy and access controls.

Design, build, and maintain high-throughput ETL/ELT pipelines for data ingestion, processing, and storage solutions.
Develop complex, performance-tuned data transformations using Python, high-performance SQL, and tools like dbt.
Implement data ops best practices, including CI/CD for data pipelines, version-controlled schemas (dbt), and automated testing.
Familiarity with modern data governance tools and practices (cataloging, lineage, and PII masking)
Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field
Familiarity with distributed processing technologies like Spark, Flink, or Kafka
Expertise in developing and debugging complex data transformations using Python and high-performance SQL
2+ years of experience in data engineering and distributed systems
Experience with workflow orchestration tools such as Airflow
Experience with Iceberg, Debezium, or Infrastructure-as-Code tools like Terraform for managing data infrastructure
Understanding of data modeling, ETL pipelines, and experience with data transformation tools like dbt
Hands-on experience building and scaling data stacks on cloud providers (AWS preferred), including experience with Snowflake

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