Job Description
Job Summary We are seeking a Senior Data Engineer to design, build, and optimize our enterprise data platform leveraging Snowflake and AWS. You will be responsible for architecting scalable ETL/ELT pipelines, implementing advanced data modeling, and ensuring high-performance data delivery for analytics and machine learning. The ideal candidate is an expert in Snowflake's cloud-native architecture and has a proven track record of managing complex data lifecycles in an AWS environment.
Key Responsibilities Data Pipeline Engineering Lead the design and implementation of reliable, metadata-driven ETL/ELT pipelines using dbt, Snowflake, and Python. AWS Integration Architect seamless data ingestion flows from diverse sources (APIs, RDBMS, SaaS) into Snowflake using AWS services such as S3, Lambda, and Kinesis/MSK. Advanced Data Modeling Design and optimize Star and Snowflake schemas, ensuring that data structures are high-performing, scalable, and optimized for analytical consumption. Performance Engineering Manage Snowflake compute resources (Virtual Warehouses), optimize micro-partitioning, and tune complex SQL queries to ensure maximum cost-efficiency and speed. Automation & CI/CD Drive engineering excellence by implementing version control (Git), automated testing via dbt, and continuous integration/deployment (CI/CD) workflows. Orchestration Develop and maintain sophisticated job schedules and dependencies using tools such as Airflow, Dagster, or Prefect. Data Governance & Quality Implement automated data validation, monitoring, and alerting to ensure data integrity and compliance with enterprise standards (GDPR/HIPAA). Cross-functional Collaboration Partner with Data Scientists and Business Stakeholders to translate complex requirements into technical data products and comprehensive documentation.
Technical Skills & Qualifications Snowflake Mastery 3+ years of deep hands-on experience with Snowflake architecture, including clustering, zero-copy cloning, and secure data sharing. dbt (data build tool) Expert proficiency in dbt (Core or Cloud), including the use of macros, snapshots, testing, and documentation modules. AWS Cloud Stack Strong experience with AWS S3, IAM, Lambda, and Glue. Advanced SQL & Python Mastery of SQL for complex transformations and Python for data engineering scripts and automation.
