Role : Data Engineering Manager
Experience : 5-8 years
Location : Bengaluru / Remote within India
Compensation : 28-30 LPA based on experience
Role Overview :
We are seeking an experienced Data Engineering Manager to lead our data engineering team in building and managing scalable data infrastructure that powers our education platform. The ideal candidate will have hands-on expertise in AWS, Data Lakes, Python, and big data technologies , along with proven leadership skills to mentor a team of data engineers. This role is critical in ensuring data-driven decision-making and enabling scalable analytics for India's largest early childhood education initiative.
Key Areas of Responsibility
Team Leadership and Management
- Lead and mentor a team of data engineers, fostering a culture of collaboration, innovation, and accountability.
- Provide technical guidance, career growth opportunities, and performance feedback to team members.
Data Infrastructure and Scalability
Design, build, and maintain scalable data pipelines, data lakes, and warehouses to support analytics and machine learning.Ensure data reliability, quality, and security across all data systems.Strategic Data Initiatives
Drive the adoption of best practices in data engineering, including data governance, metadata management, and data lineage.Collaborate with cross-functional teams (product, analytics, engineering) to align data infrastructure with organizational goals.Responsibilities in Detail
Architect and implement data lakes and ETL pipelines using AWS services (Glue, Redshift, S3, Athena, Lambda).Optimize data storage, processing, and retrieval for performance and cost-efficiency.Develop and enforce data governance policies to ensure compliance and data integrity.Integrate data from multiple sources (APIs, databases, streaming) into a unified analytics platform.Work closely with data scientists and analysts to enable advanced analytics and machine learning workflows.Stay updated with emerging technologies (e.g., Delta Lake, Snowflake, Spark) and advocate for their adoption where beneficial,Critical Success Factors
Technical Expertise :Proficiency in Python and SQL for data processing and pipeline development.Hands-on experience with AWS data stack (Glue, Redshift, S3, EMR, Kinesis).Strong understanding of data lake architectures, ETL / ELT frameworks, and big data technologies (Spark, Hadoop).Familiarity with data orchestration tools (Airflow, Luigi) and infrastructure-as-code (Terraform, CloudFormation).Leadership and Management :Proven experience leading and growing data engineering teams (5+ members).Ability to mentor engineers and foster a culture of continuous learning.Strategic Thinking :Demonstrated ability to align data infrastructure with business objectives.Keen eye for optimizing data workflows and reducing technical debt.Collaboration Skills :Strong communication skills to work effectively with technical and non-technical stakeholders.