Partner with Product, Analytics and Engineering to build scalable systems that help unlock the value of data from a wide range of sources such as backend databases, event streams, and marketing platforms
Lead technical vision and architecture with holistic point of view on both short-term and long-term horizons
Work with analytics to create company wide alignment through standardized metrics across the company
Work with Product and Engineering teams to support internal use cases such as financial reporting, product analytics and operational metrics
Enable external use cases like customer-facing dashboard, self-serve analytics, and next best action in product
Manage the complete data stack from ingestion through data consumption
Build tools to increase transparency in reporting company wide business outcomes
Work with DevOps to deploy and maintain data solutions leveraging cloud data technologies, preferably in AWS
Help define data quality and data security framework to measure and monitor data quality across the enterprise. Define and promote data engineering best practice

BS/MS in Engineering, Computer Science, Mathematics, or related field
7+ years in Data or Analytics Engineering
Strong problem-solving and communication skills; comfortable in fast-paced, cross-functional environments
Enterprise architecture and enterprise data architecture (data modeling and enterprise dimensional modeling)
Expert in SQL and data modeling (relational, dimensional, semantic)
Proven experience in data warehouse design, implementation, and maintenance (Snowflake)
Hands-on with DBT for modular, testable transformations
Experience with orchestration and ingestion tools: Airflow, Prefect, Airbyte, Fivetran, Kafka
Familiar with ELT, schema-on-read, DAGs, and performance optimization
Experience with AWS (S3, RDS, Redshift, etc.)
Familiar with Terraform, Docker, and containerized workflows (bonus)
Skilled in handling structured, semi-structured (e.g., JSON), and columnar formats (e.g., Parquet, ORC)
Experience building and supporting semantic layers for self-serve analytics
Proficient with BI tools like Looker, Tableau, or Sisense
Comfortable standardizing metrics and enabling trusted, consistent access to data
Proficient in Python and Unix/Linux scripting
Comfortable working with APIs (e.g., using curl)

Lead Data Engineer

Key skills