Role - Data Engineer

Experience - 3-6 yrs

Location - Bangalore

Technical Priorities

Real-time CDC: Ownership of high-throughput ingestion from RDBMS to Lakehouse using Debezium, PeerDB.
Lakehouse Architecture: Designing and optimizing table formats (Iceberg, Delta, Hudi) for both performance and storage efficiency.
Unified Compute: Developing robust ETL/ELT frameworks in PySpark and Flink (handling both batch and streaming workloads).
Infrastructure & Ops: Managing data workloads on AWS (EMR, EKS, MSK, S3) and automating everything via Gitlab/Github Actions.
Query & BI: Tuning Trino or Clickhouse to power real-time dashboards in Metabase, Superset, and PowerBI.

Requirements

Experience: 35 years in Data Engineering, specifically with distributed systems and cloud-native architectures.
Coding: Expert-level Python/PySpark and SQL.
Familiarity with Go/Java/Scala is a plus
Infrastructure: Hands-on experience with AWS (S3, EKS, MSK) and Infrastructure-as-Code.
Orchestration: Experience with Airflow or Temporal for complex workflow management.
AI-Native: Proficiency in using AI tools (Claude, Codex, Copilot) to write, test, and document code efficiently.
Systems Thinking: Ability to explain the trade-offs between different storage formats and processing frameworks.
Tech LeaderShip : Drive key tech initiatives by preparing TRD and actively involve in design reviews.
Domain Modelling - Should be hands on in designing Domain models for OLAP like Fact, Dimension and types of SCDs and OBT pattern tables.
Self Starter - Lead the team technically and bring in new ideas to contribute to the growth of the charter.
Customer First - Interact with the Product & Key Stakeholders & help them by adding value to the business workflow with data & analytics.

Data Engineer