Collaborate with technical teams to understand data requirements and deliver data integration solutions.
Migrate customer data from legacy systems, ensuring data accuracy, consistency, and minimal disruption.
Perform data modeling, including dimensional modeling, to create structured datasets for analytics and reporting purposes.
Plan and implement scalable data solutions on cloud platforms such as AWS, Snowflake, and Mongo Atlas.
Ensure data governance practices are followed to maintain data quality, integrity, and compliance with industry standards.
Implement data security best practices and ensure compliance with data privacy regulations (e.g., GDPR, CCPA).
Work in an Agile environment, collaborating with cross-functional teams and using project management tools to deliver projects effectively.

5–8+ years of experience in Data Engineering, with ownership of production-grade data platforms.
Proven ability to act as the sole or primary Data Engineer, making architectural decisions, prioritizing work, and setting and meeting delivery timelines without heavy oversight.
Bachelor’s or Master’s in Computer Science, Data Engineering, or equivalent experience
Proven experience managing and optimizing massive datasets (billions of rows / multi-terabyte tables), ensuring high performance and cost-efficiency.
Deep expertise in Snowflake and MongoDB Atlas (including Federation and CDC) as primary data stores.
Hands-on experience building real-time ETL/ELT pipelines using Kafka, Kafka Connectors, Estuary.
Familiarity with Vector Databases and the data infrastructure required to support LLM/RAG applications.
Proficiency in Python and SQL for data modeling (DBT), query tuning, and general automation.
Experience with AWS and containerized environments (Docker/Kubernetes).
A collaborative communicator who is adaptable in a fast-paced DevOps environment.

Senior Data Engineer

Key skills