About The Company

We are a fast-growing Instant Personal Loan platform for young professionals, enabling users to apply for quick, convenient personal loans up to ?3 Lakhs based on their needs. Our mission is to become the first choice for young professionals when it comes to seamless, transparent, and instant access to credit.

At scale, our technology platform processes millions of data points across onboarding, underwriting, disbursals, repayments, collections, customer engagement, and compliance. Building high-quality, trusted, and well-governed data systems is core to our business success.

Role Summary

We are looking for a Product Manager Data Platform & Governance to own and drive the product vision for Data Sanity, Data Flow, and Data Usage across our entire technology ecosystem. This role sits at the intersection of product, engineering, data science, analytics, compliance, and business teams.

Key Responsibilities

Data Sanity & Quality :

• Define and own data quality standards (accuracy, completeness, timeliness, consistency)
• Build product capabilities for validation, anomaly detection, reconciliation, and automated sanity checks
• Drive root-cause analysis for data issues and ensure systemic fixes
• Partner with Engineering to build data quality monitoring and alerting dashboards

Data Flow & Platform Design

• Own data dictionary and data contracts across systems
• Own and continuously improve end-to-end data flow architecture across internal systems (onboarding, underwriting, loan management, collections, CRM, finance) and 3rd-party vendors
• Define requirements for pipelines, event tracking, lineage, observability
• Ensure data flows are real-time, scalable, fault-tolerant, and cost-efficient

Data Usage & Enablement

• Build systems to monitor and measure data usage across teams
• Enable self-serve analytics and standardized data products (data marts, curated datasets)
• Drive adoption of trusted, certified datasets across business and ops teams

Governance, Privacy & Compliance

• Define, monitor, and demonstrate data governance, consent management, masking, access controls, and audit trails
• Ensure compliance with RBI, DPDP Act, and internal security policies
• Partner with Risk, Legal, and Compliance to operationalize policies into platform features

Strategy & Stakeholder Management

• Own the Data Platform roadmap aligned to company OKRs
• Translate business and regulatory needs into clear PRDs
• Track and report data quality SLAs, platform reliability, and business impact

Must-Have

What Were Looking For :

• 5+ years of Product Management experience (fintech, data platforms, or large-scale systems preferred)
• Strong understanding of databases (SQL), data modeling, and data warehouses/lakes
• Hands-on understanding of cloud architecture (AWS/GCP), scalability, reliability, and cost optimization
• Strong knowledge of security fundamentals: access controls, PII protection, encryption, audit trails
• Deep understanding of data pipelines (batch + streaming), event-driven systems, and observability
• Ability to design self-serve data products for operational teams (dashboards, curated datasets, data marts)
• Proven experience working with Engineering, Data, Risk, Ops, and Business teams
• Strong analytical thinking and communication skills

Good To Have

• Fintech / lending experience
• Data governance and privacy exposure
• Scripting and Python know-how for fast data analysis

Current Tech Stack & Expectations

• Tech Stack: Serverless architecture, MySQL, Snowflake, Trino, Athena
• Ability to work with and evaluate open-source technologies (Kafka/NATS, Airflow/dbt, observability stacks)
• Comfort collaborating with platform engineers on cloud-native and event-driven architectures
• Ability to translate platform capabilities into self-serve data products for ops teams

Additional Expectations

• Strong understanding of system design, APIs, data models, and distributed systems
• Ability to enhance/review PRDs with technical depth (schemas, data contracts, SLAs/SLOs)
• Ability to review technical designs and challenge assumptions on scalability, reliability, security, and cost
• Act as a strong bridge between Engineering, Data, Risk, and Ops

Success Metrics

• Reduction in data defects and data-related incidents
• Demonstrable improvement in data governance maturity (consent, masking, access controls, audit trails)
• Adoption of self-serve dashboards and certified datasets
• Improved data freshness SLAs, pipeline reliability, and query performance

(ref:hirist.tech)