Data Engineer
Location: Bengaluru, India
- Build and scale reliable data pipelines across batch and real-time systems
- Design and maintain data lakes and warehouses on AWS/GCP for high performance and availability
- Develop scalable ETL/ELT workflows across diverse data sources and formats
- Optimize data architecture for performance, cost, and reliability
- Implement real-time data processing within a microservices ecosystem
- Create reusable data assets and tools for analytics and data science teams
- Design efficient data models aligned with product and business needs
- Drive automation and continuously improve data workflows and infrastructure
- Partner with Product, Business, and Data teams to solve high-impact problems
- Establish and enforce best practices across data engineering, including CI/CD, testing, and monitoring
- Define and maintain data taxonomy, metadata, and documentation standards
- Evaluate and adopt new technologies to strengthen our data stack
- 3+ years of hands-on experience in data engineering
- Strong experience building and scaling data pipelines and architectures
- Proficiency in big data technologies (Spark/Flink, Kafka/Kinesis, Hive, etc.)
- Strong SQL skills and experience with both relational and NoSQL databases (e.g., Elasticsearch, Cassandra, MongoDB)
- Experience with workflow orchestration tools (Airflow, Luigi, etc.)
- Solid exposure to AWS or GCP ecosystems
- Experience with real-time/stream processing systems
- Proficiency in programming languages like Java or Scala
- Strong understanding of data modeling, metadata, and data governance principles
- Experience working with large-scale structured and unstructured datasets
- Familiarity with scalable data stores and distributed systems
- Strong problem-solving skills and ability to work in a fast-paced, cross-functional environment
- Good understanding of engineering best practices (Agile, CI/CD, testing frameworks)
- High ownership: You build it, you own it
- Build at the Core of Fintech Infrastructure
- Strong engineering culture: Clean code, solid architecture, fast execution
- Zero fluff: Minimal bureaucracy, maximum impact
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