About the job
About CodeNinja
CodeNinja is a leading full-stack AI delivery firm dedicated to empowering enterprises, government bodies, and software acquirers in the creation and management of intelligence-driven systems that streamline mission-critical workflows. Our expertise lies in integrating AI into active operations, leveraging robust engineering principles alongside AI-native delivery techniques to provide measurable value, resilience, and sustainable ownership for our clients. With a global presence bolstered by AI Labs, AI Pods, and Global Capability Centers, we facilitate collaborative engineering of scalable platforms across diverse regions and time zones.
Role Overview
We are on the lookout for a talented Data Engineer with a minimum of 3 years of experience to architect and construct resilient, scalable data pipelines that support financial machine learning forecasting models.
Your key responsibilities will include the ingestion, cleaning, validation, and structuring of complex multi-source financial datasets, ensuring high-quality training for time-series models and analytics.
This position demands strong technical knowledge in ETL/ELT pipelines, financial data processing, and data quality assurance within secure corporate contexts.
Key Responsibilities
- Design, develop, and maintain automated ETL/ELT pipelines from CSV/Excel exports within secure enterprise settings.
- Conduct data cleaning, normalization, validation, and integrity checks on financial transaction datasets.
- Perform entity mapping, currency standardization, and data synthesis across various legal entities and general ledger accounts.
- Establish exploratory data analysis (EDA) pipelines, including statistical analyses of financial flow patterns and seasonality.
- Develop feature stores featuring pre-computed lag and rolling statistics for machine learning forecasting applications.
- Guarantee high data quality through rigorous validation frameworks and automated integrity checks.
- Document data pipelines, quality reports, and technical handoff materials for machine learning engineering teams.
- Work collaboratively with machine learning engineers, subject matter experts, and stakeholders.

