Data Engineering Manager ()

This role leads the architecture and evolution of analytics and observability platform. The position combines hands-on technical leadership with team mentorship to deliver high-throughput data pipelines, real-time visibility, and scalable lakehouse systems supporting mission-critical AI workflows.

Key Responsibilities:
Lead and mentor a data engineering team driving scalable, reliable platform delivery
Architect high-volume batch and streaming data pipelines across diverse data sources
Own real-time data lakes and analytics-ready warehouse architectures
Optimize Kafka-based streaming systems for throughput, latency, and reliability
Implement observability metrics, dashboards, and alerts for operational KPIs
Collaborate with product and platform teams on data contracts and integrations
Drive research and adoption of modern streaming and lakehouse technologies

Skills
10+ years experience in data engineering or backend engineering
Should have a product based company experience
2+ years in a technical leadership or team-lead role
Expert-level experience with Kafka for high-throughput streaming systems
Strong hands-on expertise with PySpark for distributed data processing
Advanced experience with AWS Glue for ETL orchestration and metadata management
Proven experience building and upgrading real-time data lakes at scale
Hands-on knowledge of data warehouses such as Redshift or Snowflake
Experience with AWS services including S3, Kinesis, Lambda, and RDS