Data and PII-Analyst

Designation: Data and PII Analyst

Experience: 5 to 8 years

Qualification: BE/B Tech, ME/M Tech, BSc, MSc

Location:  Mumbai worli

As a Data and PII Analyst, you will analyse a bank's stored procedures and batch integrations (Informatica and PySpark) to map how personal data flows through each interface, identify the PII in it, and propose how each column is protected, so the applications meet DPDP requirements.

You will work in a pod with integration engineers, and with application owners, current vendors, the bank's data owners and the DPO's office, turning analysis into an interface inventory, signed-off PII decisions and masking choices.

• Analyse stored procedures, Informatica exports and PySpark code to list each interface's sources, targets and schedule.

• Trace how personal data moves through each procedure and integration, and where batches read or write it.

• Prepare the interface inventory and classification: table or file, needs real values or not, consent-dependent or not.

• Run walkthroughs with application owners and current vendors to confirm the analysis.

• Propose PII columns from DDL and code, and masking per vault policy.

• Get data owners' sign-off on PII decisions for each wave.

• Prepare PII mapping in PrimeSoft's template for configuration.

• Use AI to read procedures and integration code and propose PII columns, confirmed by data owners against the bank's own PII policy.

• Expert-level SQL, including reading and analysing complex stored procedures.

• Ability to read and analyse Informatica and PySpark integrations; Impala.

• Understanding of batch schedules, dependencies and data lineage.

• Demonstrated hands-on experience using LLMs (Claude, Cursor) as a core part of delivery, including:

  • Prompt engineering

  • Context engineering

  • Token optimisation and context management

  • Guardrails and hallucination mitigation

  • AI reading stored procedures, Informatica exports and PySpark code to list sources, targets and schedules

  • AI drafting configuration and test cases, reviewed and signed off by people

Candidates should have practical experience integrating AI into engineering workflows rather than limited exposure or experimentation.

• Strong communication and stakeholder management skills.

• Ability to challenge requirements constructively and provide technical guidance.

• Strong ownership mindset with the ability to make informed technical decisions independently.

• Bias toward execution, continuous improvement, and delivering business value.

• Exposure to fintech, banking, payments, or financial services data platforms.

• Experience with DPDP data protection programmes.

• Experience with file-based interfaces.

• PII identification and classification.

• Understanding of masking, tokenisation and encryption.

• Understanding of consent, purpose and data-principal rights under DPDP.