Data Engineer - DWH at FairMoney (India)

FairMoney, a leading mobile banking institution established in 2017, specializes in extending credit and comprehensive financial services to emerging markets, with primary operations in Nigeria. Backed by nearly €50 million in funding from investors like Tiger Global, DST, and Flourish Ventures, FairMoney is building a premier mobile banking and Point-of-Sale (POS) platform. Our services have expanded from digital microcredit to include current accounts, savings, debit cards, and advanced POS solutions for merchants and agents.

Join our diverse, multicultural team of over 27 nationalities as we reshape financial services for underserved communities globally. This role offers an opportunity to significantly impact our mission of financial inclusion.

Role and Responsibilities

As a Data Engineer focusing on Data Warehouse (DWH), you will be crucial in building and maintaining robust data infrastructure. Key responsibilities include:

• Design and maintain batch and streaming data pipelines from raw data ingestion to warehouse-ready tables.
• Build and manage staging, source, and transformation layers within the data warehouse.
• Implement efficient incremental loading strategies for large datasets.
• Handle complex data scenarios like append-only data, late-arriving events, and change data capture.
• Develop efficient SQL transformations, merge, and upsert logic.
• Design and maintain critical data structures: fact tables, dimension tables, and snapshot tables.
• Ensure high data quality through rigorous validation checks, monitoring, and reconciliation processes.
• Support schema evolution and implement backward-compatible changes.
• Optimize data pipeline performance, cost-efficiency, and reliability.
• Collaborate closely with analytics, product, finance, and engineering teams to deliver accurate and usable data.
• Document data pipelines, data models, and data contracts comprehensively.
• Actively participate in debugging data issues and enhancing pipeline observability.

Technical Requirements

• Strong SQL skills: Proficient in joins, window functions, aggregations, incremental logic, and performance tuning.
• Experience in building data pipelines using Python or similar scripting languages.
• Hands-on experience with at least one modern data warehouse solution (e.g., BigQuery, Snowflake, Redshift).
• Familiarity with data orchestration tools (e.g., Airflow, Composer).
• Understanding of incremental pipelines, partitioning strategies, and idempotent processing.
• Experience in designing and maintaining data models optimized for analytics.
• Knowledge of deduplication, late data handling, and merge strategies.
• Proficiency with version control systems (Git) and collaborative development workflows.

Preferred Qualifications (Good-to-Have)

• Experience with dbt or similar data transformation frameworks.
• Exposure to streaming data systems (e.g., Kafka, Kinesis).
• Experience in migrating or refactoring existing data pipelines.
• Familiarity with implementing data quality frameworks or monitoring solutions.
• Proficiency in Shell Scripting and CI/CD practices for data pipelines.
• Prior experience in a fintech, lending, or transactional data environment.

Experience Requirements

We are seeking candidates with 2-5 years of experience working with production data pipelines and warehouse systems, demonstrating the ability to:

• Collaborate effectively with analytics and product teams.
• Design core data warehouse tables independently.
• Debug complex data issues and resolve them.
• Support the evolving data needs of a rapidly growing organization.
• Prioritize correctness and data integrity over quick fixes.
• Understand and articulate trade-offs between batch, incremental, and streaming data processing.
• Write clear, maintainable SQL and robust pipeline logic.
• Rigorously test and validate changes before deployment.
• Take full ownership of data quality and reliability.
• Continuously improve pipeline design and observability.

Benefits

• Training & Development opportunities
• Family Leave (Maternity, Paternity)
• Paid Time Off (Vacation, Sick & Public Holidays)

Recruitment Process

1. Screening Interview with Talent Acquisition team (30 minutes)
2. Take-home Assignment
3. Technical Interview - SQL/Python proficiency with Tech. Team (60 minutes)
4. Technical Design Interview with Shubham Jain (60 minutes)