Role Description

• Design and implement scalable, reliable, and cost-effective data pipelines and data platforms.

• Build and maintain ETL/ELT pipelines, data models, and APIs using Python, SQL, and modern cloud tools.

• Own data ingestion, storage, transformation, orchestration, and monitoring in cloud environments.

• Guide engineers, conduct code reviews, and drive best practices in data engineering.

• Partner with Data Science, Analytics, Product, and Business teams to translate requirements into technical solutions.

• Drive end-to-end delivery of data projects from requirements gathering to production deployment and support.

• Implement testing, monitoring, and data quality frameworks.

• Enforce data security, privacy, and compliance standards.

• Optimize pipeline performance, cost, and reliability.

• Troubleshoot production issues.

Requirement

• Masters or Degree in Computer Science, Engineering, Mathematics, or related field.

• Equivalent practical experience will be considered.

• Has at least 6+ years in data engineering, with 2+ years in a lead/senior role.

• Strong in Python and SQL. Experience with PySpark is a plus.

• Hands-on experience with AWS, GCP, or Azure. Services like S3/ADLS, BigQuery/Redshift/Snowflake, Airflow, Kafka, DBT.

• Strong experience with dimensional modeling, data warehousing, and lakehouse architectures.

• Experience with Airflow, Prefect, or similar workflow tools

• Engineering practices: CI/CD, Docker, Git, Infrastructure as Code, unit testing.

• Good to Have: Kafka, Spark Streaming, Flink; Databases: PostgreSQL, MongoDB, Redis.

• Working with feature stores and model deployment pipelines.

• Experience hiring, mentoring, and doing technical interviews.

• Can explain complex technical concepts to non-technical stakeholders.

• Proactive problem solver who takes end-to-end ownership.

• Works well across Data Science, Analytics, and Engineering teams.

• The role will need to secure a clearance, due to the sensitivity of the project.