In this role, you’ll make an impact in the following ways :

- Data Quality Control Execution :Build and implement Detective Data Quality Controls (DQCs) based on approved Data Quality Requirements (DQRs), run and monitor data quality controls in BAU, ensure controls align with defined business rules and relevant data quality dimensions, and maintain control logic as upstream data structures or source systems evolve.
- Multi‑Technology Data Quality Execution :Implement and operate data quality controls across Snowflake (SQL-based checks for platform datasets), SQL-based data sources outside Snowflake, and file-based data feeds including vendor and batch files; perform common file-based validations such as record count and reconciliation checks, mandatory field and null value checks, and basic format or structural validation; and ensure control outputs are consistent, interpretable, and suitable for effective operational monitoring.
- Investigation & Operational Support :Support L2 data quality investigations when controls fail or issues are escalated by analyzing data using SQL and file-based validation to determine whether failures represent genuine data issues, control logic defects,



or upstream source or transformation changes; document findings clearly and update data quality incidents in ServiceNow; and escalate complex or structural issues to more senior team members where appropriate.
- Team Capability & Knowledge Sharing :Share technical knowledge and good practice within the Data Quality Management (DQM) team, support colleagues with SQL used for data quality controls and investigations, file-based data validation techniques, and understanding common control patterns and investigation approaches; contribute to documentation, examples, and reusable templates; provide informal coaching and peer support without formal line management responsibility; and work with Data Stewards and engineers to clarify rule intent where market data behavior is relevant.

To be successful in this role, we’re seeking the following : Essential Skills:

- Robust SQL skills
- Experience running data quality checks
- Experience with Snowflake and file-based data
- Comfortable operating in BAU environments

Additional Skills:

- Bloomberg or market data exposure
- Securities or financial data experience
- Grafana or monitoring tool exposure
- Experience or exposure to Collibra for data cataloguing, lineage, classifications, and supporting data governance processes

📌 Senior Associate, Production Services Application Support Analyst (Pune)
🏢 The Bank of New York Mellon
📍 Pune