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Salary
≈ $22k – $45k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior

First seen by Alion on Aug 26, 2026.

Overview
Company
Impact
Profile match
Nineleaps are enablers of Digital Transformation across industries, helping partner companies become future ready.

We're looking for a Senior Data Engineer with strong data architecture and system design skills to design, build, and own end-to-end production pipelines powering analytics and decision-making across the org. The role centres on deep Python + SQL expertise, robust transaction-level fact tables, and rigorous data quality and auditability, ideally within a finance, risk, or compliance data domain. Internal candidates familiar with Uber's data platform stack (Piper/uWorc, Databook, uSecret, DSW, SourceGraph, Query Builder, OneETL) will be prioritised.

Responsibilities:

  • Own data architecture and system design decisions for pipelines and data models - grain, partitioning, schema evolution, and scalability trade-offs.
  • Take end-to-end ownership of pipelines in production: design, build, deploy, monitor, and operate.
  • Design and build transaction tables (append-only, immutable event/transaction-grain fact tables) alongside standard fact/dimension models using medallion (bronze/silver/gold) architecture.
  • Build and support pipelines for finance, risk, or compliance use cases where accuracy, auditability, and data lineage are critical.
  • Implement data quality and auditability controls: validation checks, reconciliation logic, anomaly detection, and audit trails for every pipeline you own.
  • Write complex, cross-dialect SQL (MySQL + PostgreSQL): window functions, multi-layered CTEs, CASE-driven logic, COALESCE/NULLIF, type casting, and date/timestamp handling.
  • Build idempotent reload patterns (DELETE+INSERT), UNION ALL/set operations, and templated (Jinja-style) SQL including handling for late-arriving/corrected transaction records without double-counting.
  • Orchestrate Airflow-style DAGs (Pipeline/BaseTask, ExternalTaskSensor for cross-pipeline deps) with secure credential handling via uSecret.
  • Build Python ETL tooling: pandas (CSV DB), SQLAlchemy + raw drivers (MySQLdb, psycopg2), type hints/dataclasses, class-based pipeline design.
  • Integrate with Google Drive/Sheets APIs; build YAML-driven pipeline configs; handle Piper staging/user_staging quirks.
  • Own CI/CD for pipelines via standard Git workflows.
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