Engineer

Data Engineer - Financial Technology

Join Indus Valley Partners in Noida as a Data Engineer to build and maintain robust data solutions within the financial technology sector. This role focuses on developing scalable ETL/ELT pipelines, optimizing SQL queries, and designing data warehouse schemas for financial time-series and transactional data. You will ensure data accuracy and performance, especially during critical trading and reporting periods. Experience with financial domain concepts like asset classes, P&L attribution, risk metrics, and general ledgers is a plus, as is ensuring compliance with financial regulations such as SOX, GDPR, and PCI-DSS. You'll also establish data quality frameworks and maintain comprehensive documentation for data lineage and dictionaries. This position requires translating complex business logic into effective technical data transformations.

Key Responsibilities:

1. ETL & Pipeline Development

• Design, build, and maintain scalable ETL/ELT pipelines for efficient data ingestion.
• Implement robust data validation and error-handling mechanisms to guarantee the precision of financial data.

2. Database & SQL Architecture

• Write and optimize advanced SQL queries for data extraction, manipulation, and in-depth analysis.
• Design and manage data warehouse schemas (e.g., Star/Snowflake) specifically for financial time-series data and transactional records.
• Conduct performance tuning on large-scale datasets to minimize latency during peak trading hours or reporting periods.

3. Financial Domain Implementation (Advantageous)

• Collaborate with Quantitative Analysts and Finance teams to model intricate financial concepts, including Asset Classes, P&L attribution, Risk Metrics, and General Ledgers.
• Ensure all data architecture adheres strictly to financial regulations and compliance standards like SOX, GDPR, and PCI-DSS.
• Translate business logic concerning trades, settlements, and positions into precise technical data transformations.

4. Data Quality & Governance

• Establish data quality frameworks to proactively detect anomalies in financial transactions.
• Maintain detailed documentation of data lineage, data dictionaries, and metric definitions to facilitate audit requirements.