Senior Data Engineer

MatchPoint Solutions is a fast-growing, young, energetic global IT-Engineering services company with clients across the US. We provide technology solutions to various clients like Uber, Robinhood, Netflix, Airbnb, Google, Sephora, and more! More recently, we have expanded to working internationally in Canada, China, Ireland, UK, Brazil, and India. Through our culture of innovation, we inspire, build, and deliver business results, from idea to outcome. We keep our clients on the cutting edge of the latest technologies and provide solutions by using industry-specific best practices and expertise.

We are excited to be continuously expanding our team. If you are interested in this position, please send over your updated resume. We look forward to hearing from you!

Job Title: Senior Data Engineer

Location: Bengaluru, India (Onsite)

Employment Type: Contract

Pay Range: 25-30Lpa

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 centers 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 prioritized.

What You'll Do

• Own data architecture and system design decisions for pipelines and data models - grain, partitioning, schema evolution, and scalability tradeoffs.
• 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.
• Use Uber's internal stack day-to-day: OneETL for pipeline authoring, Piper/uWorc for scheduling, Databook for lineage, uSecret for credentials, DSW notebooks for CSV/table work, SourceGraph for code search, Query Builder for ad hoc SQL.
• Document data models and pipeline logic for maintainability, keeping compliance/audit needs in mind.

What We're Looking For Must-Have:

• 6+ years in data engineering.
• Strong data architecture and system design skills to make sound tradeoffs on modeling, partitioning, and scalability, not just write queries.
• Demonstrated end-to-end production ownership of data pipelines from design through deployment, monitoring, and operational support.
• Expert-level Python + SQL as the primary toolset. Hive/Hadoop is a nice-to-have only.
• Robust data quality and auditability practices - validation, reconciliation, anomaly detection, audit trails.
• Experience working with finance, risk, or compliance data - comfort with the heightened accuracy, auditability, and data-lineage requirements typical of these domains.
• Proven experience designing and building transaction/fact tables.
• Expert SQL (window functions, CTEs, CASE logic, cross-dialect MySQL/PostgreSQL).
• Strong Python (pandas, SQLAlchemy, type hints/dataclasses, class-based pipelines).
• Airflow-like orchestration experience (Piper/uWorc a plus).
• Solid data modeling fundamentals (fact/dim, normalization, medallion architecture).
• YAML-based config experience; standard CI/CD and Git workflows.

Strongly Preferred (Uber Internal Tooling):

Piper/uWorc, Databook, uSecret, DSW, SourceGraph, Query Builder, OneETL.

Nice-to-Have:

Hive/Hadoop or Spark experience.

MatchPoint Solutions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.