Data Engineer

We are looking for a Data Engineer based in Bangalore, India, to join our growing platform engineering team. You will play a key role in designing and building data platforms that drive MerQube's index computation ecosystem. As a Data Engineer at MerQube, you will be responsible for building reliable, secure, and scalable data infrastructure to ingest, process, and manage financial data from multiple global sources.

Responsibilities:

• Design, build, and maintain scalable data pipelines to ingest and process large volumes of structured and unstructured financial data.
• Develop robust data workflows supporting index computation, analytics, and research.
• Work closely with backend engineers, quant researchers, and platform teams to deliver high-quality data solutions.
• Ensure data quality, consistency, lineage, and validation frameworks across pipelines.
• Optimize ETL processes for performance, resilience, and cost efficiency.
• Build and manage data storage systems, including relational databases, data lakes, and warehouse environments.
• Implement monitoring, logging, and alerts for data operations.
• Contribute to architecture discussions and technical design decisions.
• Support production environments, troubleshoot issues, and participate in on-call rotations as required.

Requirements:

• Bachelor's Degree in Computer Science, Engineering, or Mathematics, or equivalent experience.
• 4+ years of experience as a data engineer or in a similar role.
• Strong programming experience in Python (preferred) and/or Java/Scala.
• Experience building and maintaining ETL / ELT data pipelines.
• Hands-on experience with SQL and database design (RDBMS or NoSQL).
• Experience working with cloud platforms (AWS or GCP).
• Familiarity with data workflow orchestration tools such as Airflow or similar.
• Strong understanding of data structures, data modeling, and data lifecycle management.

Preferred Qualifications:

• Experience working with financial market datasets or data-intensive platforms.
• Familiarity with pandas, numpy, or scientific computing stacks.
• Experience with streaming data systems (Kafka, Pub/Sub, etc. ).
• Experience with Docker, Kubernetes, and containerized environments.
• Knowledge of data security, governance, and compliance best practices.
• Exposure to big data technologies (Spark, BigQuery, Redshift, Snowflake, etc. ).