Data Engineering Manager is responsible for leading, hiring, developing and building data engineering team and providing them technical direction. This position ensures business objectives aligning to Data & Analytics strategy and policy are accomplished through established, implemented and executed plans & tactics. The position focuses on coaching and developing staff, communicating organization objectives and expectations, and monitoring the progress of projects and initiatives, taking appropriate action when needed.

• Proficiency in Python, Spark, SQL, DataOps.live/DBT, Snowflake
• Prior experience supporting Data Governance initiatives desired: Data Quality, Metadata Management (Data Cataloging, Data Lineage), Master Data Management, Data Security
• Proficiency in other software engineering languages and/or methodologies
• Strong knowledge of working Unix Shell scripting
• Provide ongoing support for the existing ETL processes, procedures, as well as the ability to design, code and deploy new ETL pipelines to support application and business needs.
• Develop an effective working relationship with Data Analysts, Data Architects, Database Administrators, and others in designing, developing, and implementing robust data management solutions.
• Demonstrate the ability to translate business rules into ETL code while adhering to best ETL development and design practices.
• Demonstrate proficiency in SQL language, as well as working knowledge of common transformation methodologies, techniques, and tools.
• Ability to design and build enterprise grade reusable frameworks.

• Bachelor of Engineering degree with 15 years of Data Engineering experience with pertinent ETL tools (e.g. DBT, Informatica, Data Stage, etc.) or big data stack
• Lead Data Warehouse/Business Intelligence development team in building and supporting Analytical solutions.
• Lead the design of data pipeline/ETL using emerging technologies and tools
• Drive the team to develop operationally efficient analytic solutions
• Design and build highly scalable data pipelines using new generation tools and technologies like AWS, Snowflake, Spark, Kafka to induct data from various systems
• Translate complex business requirements into scalable technical solutions meeting data warehousing design standards.
• Solid understanding of analytics needs and proactive-ness to build generic solutions to improve the efficiency
• Lead/ work with many global teams, communicate effectively, both written and verbal, with technical and non-technical multi-functional teams
• Should have strong ability to manage stakeholder communication & risk management
• Thrives in a dynamic environment, maintaining composure and a positive attitude