Sr Data Engineer
Matchpoint Solutions (company site, opens in a new tab)6+ yrsBengaluruOn-siteDirect career pageEst. 25–30 LPA
Posted 21h ago
Reach people at Matchpoint Solutions
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Matchpoint Solutions you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own end-to-end design, build, deployment, monitoring, and operation of production data pipelines powering finance, risk, or compliance analytics.
- Design and manage data architecture decisions including transaction-level, append-only fact tables using medallion architecture ensuring accuracy, auditability, and data lineage.
- Implement robust data quality controls (validation, reconciliation, anomaly detection, audit trails) and orchestrate workflows using Airflow-like tools and Python ETL tooling.
Minimum Requirements
- 6+ years of data engineering experience with end-to-end pipeline ownership.
- Strong expertise in Python (pandas, SQLAlchemy, type hints/dataclasses) and SQL (MySQL, PostgreSQL, window functions, CTEs, cross-dialect).
- Experience with finance, risk, or compliance data domains requiring accuracy and auditability.
- Location: Bengaluru, India (Onsite); Employment Type: Contract; Pay Range: 25-30 LPA.
Ideal Candidate Profile
- Experienced in designing and scaling data models with medallion architecture and transaction/fact tables.
- Familiarity or experience with Uber’s internal data platform tools (Piper/uWorc, Databook, uSecret, etc.) is strongly preferred.
- Comfortable handling complex data orchestration, CI/CD pipelines, and maintaining rigorous data quality and audit practices in production environments.