Data Engineering Manager

• Collaborate with data analysts and business/product teams to understand requirements and deliver data ready for analysis and reporting.
• Establish define and advocate for data governance including data quality testing documentation coding best practices and peer reviews.
• Continuously source transform test deploy and document data models and data sources.
• Work closely with the infrastructure team to enhance and optimize the data infrastructure.
• Develop and execute a comprehensive data roadmap staying attuned to industry trends and advancements.

• Design and implement scalable highperformance and reusable data models for the data warehouse ensuring endusers get consistent and reliable answers during analysis.
• Focus on testdriven design to deliver repeatable and maintainable processes and tools.
• Create and maintain an optimal data pipeline architecture and data flow logging framework.
• Build data products features tools and frameworks that empower data and analytics teams.

• Lead project by prioritizing effectively and allocating resources efficiently.
• Address and resolve technical blockers through expertise negotiation and delegation.
• Ensure timely project completion by facilitating standups and making necessary course corrections.

• Lead and mentor a team of 58 members fostering growth and development.
• Conduct regular oneonones to ensure the wellbeing of the team.
• Provide periodic assessments and actionable feedback for improvement.
• Recruit and onboard new team members with a focus on longterm resource planning collaborating closely with the hiring team.

• Set the standard for the quality of technical and datadriven solutions produced by the team.
• Enforce code quality standards and promote effective code review practices as a tool for growth.
• Establish communication channels and feedback loops for knowledge sharing and stakeholder management.
• Explore and implement the latest best practices and tools to continuously upskill the team.

• Analytics: Python R SQL Excel PPT Google Colab
• Database: PostgreSQL Amazon Redshift DynamoDB Aerospike
• Warehouse: Snowflake S3
• ETL: Airflow DBT Custom Python solutions Amundsen (Discovery)
• Business Intelligence/Visualization: Metabase Google Data Studio
• Frameworks: Spark Dash Streamlit
• Collaboration: Git Notion

• Minimum of 9 years of industry experience with at least 5 years in a data engineering role.
• Proven experience managing a team of 4 or more developers endtoend.
• Strong handson expertise in data modeling and data warehousing.
• Solid technical background with the ability to contribute to design and review processes.
• Strong experience applying software engineering best practices to data and analytics including version control testing and CI/CD.
• Exceptional attention to detail especially regarding data quality issues.
• Excellent time management and proactive problemsolving skills to meet critical deadlines.
• Familiarity or expertise with the listed analytics stack or similar tools and frameworks.

sql,technical leadership,git,airflow,postgresql,notion,data quality,data models,project management,data governance,python,data warehouse design,snowflake,r,dbt,process design,team management,dynamodb,excel,data strategy,data roadmap,data pipeline architecture,spark,amazon redshift,data infrastructure,scalable data models