Lead building of scalable, fault-tolerant pipelines with built-in data quality checks that transform, load, and curate data from various internal and external systems.
Provide leadership to cross-functional initiatives and projects.
Influence architecture design and decisions.
Build cross-functional relationships with Data Scientists, Product Managers, and Software Engineers to understand data needs and deliver on those needs.
Improve engineering processes and cross-team collaboration.
Ruthlessly prioritize work to align with company priorities.
Provide thought leadership to grow and evolve the Data Engineering function and the implementation of SDLC best practices in building internal-facing data products by staying up-to-date with industry trends, emerging technologies, and best practices in data engineering.
This role requires
5+ years experience and knowledge of building data lakes in AWS (e.g., Spark/Glue, Athena), including data modeling, data quality best practices, and self-service tooling.
Development experience in at least one object-oriented language (Java, Python, R, Scala, etc.).
Expert at SQL
Strong experience with dbt, Airflow.
Experience in BI and Data Warehousing.
Experience with Apache Iceberg tables.
Expertise in architecting and building solutions on any of the databases (Cassandra, DynamoDB, Elasticsearch, Aurora, Redshift etc)
Experience with automation and Orchestration tools
Built enterprise products from scratch, worked at product based companies
Mentor junior Engineers on the team
Has a high bar on Design, Architecture for scalable systems
Experience with CI/CD tools such as Jenkins, Gitlab CI etc
Demonstrated success leading cross-functional initiatives.Passionate about data quality, code quality, SLAs, and continuous improvement.
Deep understanding of data system architecture.
Deep understanding of ETL/ELT patterns.
Bonus points if you have
Experience with FinOps industry and best practices
FinOps Certified Practitioner preferred Infrastructure Cost and efficiency