Manager Data Engineering

Job Description

We are looking for an experienced and dynamic Manager - Data Engineering to lead the design, development, and optimization of scalable data pipelines and analytics platforms. This role demands a hands-on leader with deep expertise in AWS cloud services and Snowflake, who can drive end-to-end data engineering initiatives while mentoring a high-performing team. You will collaborate closely with cross-functional teams including Data Science, Product, and Engineering to deliver robust and secure data solutions that enable business insights and decision-making at scale. Responsibilities: Design and build scalable, efficient, and secure data pipelines and ETL/ELT processes using AWS services (Glue, S3, Lambda, Redshift, Athena, etc.) and Snowflake. Implement and optimize data warehousing solutions in Snowflake to support BI, analytics, and ML workloads. Ensure proper data modeling, metadata management, and version control for data assets. Design and build scalable, efficient, and secure data pipelines and ETL/ELT processes using AWS services (Glue, S3, Lambda, Redshift, Athena, etc.) and Snowflake. Implement and optimize data warehousing solutions in Snowflake to support BI, analytics, and ML workloads. Ensure proper data modeling, metadata management, and version control for data assets. Lead and mentor a team of data engineers; set goals, conduct performance reviews, and foster professional growth. Drive the data engineering roadmap, aligning technical initiatives with business priorities. Collaborate with stakeholders to define data strategy, architecture, and scalable best practices. 8+ years of experience in Data Engineering, with at least 2+ years in a leadership/managerial capacity. Proven expertise in AWS Data Stack (Glue, S3, Redshift, Lambda, EMR, etc.). Hands-on experience with Snowflake including data modeling, performance tuning, and role-based access. Strong programming skills in Python, SQL, and Spark. Familiarity with orchestration tools (Airflow, Step Functions) and data versioning tools (DVC, LakeFS). Experience with CI/CD practices and Infrastructure-as-Code. Excellent communication, stakeholder management, and cross-functional collaboration skills.