Data Quality Manager

About The Role:

We are looking for a Data Quality Manager — India who will own, build, and continuously improve Valocity's data quality capability across its India operations.

 

This is a high-ownership role that goes well beyond monitoring and reporting. The Data Quality Manager will design the governance frameworks, define the data standards, lead the quality engineering agenda, manage external data source relationships from a quality perspective, and serve as a key internal owner for data trust and data fitness-for-purpose across Valocity’s India data estate.

 

The role sits at the intersection of data governance, product quality, software engineering, QA, data engineering collaboration, and client assurance. It requires someone who can think strategically about data quality as a product differentiator, operate hands-on at a technical level when the situation demands, influence engineering and product teams without direct authority, and communicate data quality matters clearly to lender clients, internal leadership, and audit / assurance stakeholders where required.

 

The role requires someone who can operate both strategically and operationally: setting up scalable quality practices while also diving into data issues, root-cause analysis, stakeholder communication, and continuous improvement.

 

This is initially expected to be a hands-on individual contributor / functional ownership role, with the opportunity to mentor analysts or build a small data quality capability as the function grows.

 

As Valocity scales its data partnerships, builds new data products for lenders, and introduces AI-enabled capabilities into its platform, this role will play a critical part in ensuring that the underlying data remains reliable, usable and trusted. While the immediate mandate is India-focused, strong performance in this role may create opportunities to support broader regional data quality initiatives as Valocity expands in other markets.

 

  

Key Responsibilities:

1. 
   Data Quality Strategy & Governance

Design Valocity India's data quality strategy — establishing governance structures, standards, and frameworks that scale with the platform's growing data estate.

• Define and own the India data quality vision, strategy, and roadmap — aligned with Valocity's global data and product strategy.
• Design and maintain data quality policies, data standards, field-level validation rules, and acceptance criteria for all core data entities.
• Develop and enforce data contracts between upstream data sources (APIs, portals, lender uploads, third-party feeds) and downstream consumers (product features, dashboards, reports, AI models).
• Build a data quality operating model — defining roles, responsibilities, escalation paths, SLAs, and review cadences.
• Represent data quality in product roadmap planning, data partnership evaluations, and AI capability discussions — ensuring data fitness-for-purpose is built in from the design stage.

1. 
   Data Monitoring, Validation & Anomaly Management

Maintain active, structured visibility into the health of all data flowing into and through the Valocity platform.

 

• Monitor acquisition of property valuation data across multiple channels for completeness, conformance, consistency, timeliness, and accuracy.
• Monitor supplementary external datasets such as guideline values, RERA data, project data, geospatial inputs, and partner datasets for usability and data quality.
• Design, implement, and run automated and manual validation checks for critical data fields such as property address, building / project name, location hierarchy, classification, configuration, coordinates, valuation values, ownership attributes, and source tags.
• Identify, log, prioritize, and triage anomalies, duplicates, outliers, stale records, missing values, and conflicting records, assigning severity levels and routing them to the appropriate teams.
• Continuously review data acquisition trends and sample outputs to detect emerging issues before they affect product performance, customer operations, or downstream reporting.
• Perform deep root-cause analysis on systemic data quality failures and translate findings into clear, prioritized remediation tasks for engineering and product teams.

1. 
   Quality Scorecards, Frameworks & Reporting

Ensure data quality is visible, measurable, and continuously improving — for engineering, product, leadership, and clients.

 

• Design and maintain data quality scorecards and KPI dashboards covering all critical datasets — providing real-time and periodic views into dataset health.
• Define measurement frameworks for all six dimensions of data quality: completeness, accuracy, conformance, consistency, timeliness, and uniqueness.
• Implement data profiling practices to systematically assess the shape, distribution, completeness, and anomaly profile of key datasets
• Produce weekly, monthly, and quarterly data quality reports for internal stakeholders — product, engineering, operations, customer success, and senior leadership.
• Develop executive-level data quality reporting — translating technical quality metrics into business impact narratives for senior leadership and governance forums.

 

1. 
   Data Source Management & External Partnerships

Own the data quality relationship with all external data providers and evaluate new sources with a quality-first lens.

 

• Evaluate new external data sources for coverage, quality, freshness, format maturity, and integration feasibility.
• Define quality acceptance criteria and testing protocols for all new data partnerships before production ingestion.
• Conduct comparative analysis across overlapping data sources to assess relative coverage, accuracy, and recency — supporting decisions on which sources to trust, combine, or replace.
• Manage ongoing data quality SLAs with external providers — flagging degradation, initiating remediation discussions, and escalating where provider-side issues affect product or client commitments
• Lead the geocoding and address standardisation workstream — validating output quality across geocoding engines, defining confidence thresholds, and driving continuous improvement.
• Identify gaps in Valocity's current data estate and flag enrichment opportunities that could improve platform intelligence, client analytics, or new product capabilities.

 

1. 
   Client Data Assurance & Onboarding

Ensure that client data — both supplied and delivered — meets the quality bar required for confident lender decision-making.

 

• Lead the data quality workstream for client onboarding — validating customer-supplied property data against Valocity's internal standards, identifying gaps, and defining enrichment plans before go-live
• Serve as the primary internal expert for data quality escalations — providing clear, evidence-based responses to lenders, valuers, and enterprise clients.
• Work with Customer Success and Business Development teams to position Valocity's data quality practices as a commercial differentiator in client conversations, RFP responses, and renewal discussions.
• Conduct periodic client data quality reviews — presented as a value-add advisory service for key lender accounts.

1. 
   AI-Assisted Quality & Automation

Lead Valocity's use of AI and automation to scale data quality practices beyond what manual effort can achieve.

 

• Define and drive the AI-assisted data quality roadmap — identifying where ML, NLP, and generative AI can augment, prioritise, or selectively automate data quality checks while retaining appropriate human review and control.
• Work with data engineering and product teams to embed automated data quality gates, AI-assisted validation, and anomaly detection into the platform ingestion pipeline.
• Explore and pilot LLM-based approaches for unstructured data extraction — including field extraction from valuation reports and inconsistency detection in free-text property descriptions.
• Champion advanced matching techniques — fuzzy matching, entity resolution, geospatial clustering, duplicate detection — for property master data management across India's fragmented data sources.
• Use AI tools actively in day-to-day work for documentation, report generation, issue summarisation, data analysis, and stakeholder communication.
• Stay current with AI-driven data quality tooling and approaches globally — and bring relevant innovations into Valocity's practices in a practical, commercially grounded way.

1. 
   Cross-Functional Collaboration & Stakeholder Management

Operate as a cross-functional contributor — influencing engineering, product, operations, and client-facing teams without direct authority.

 

• Collaborate with Data Engineering to define, scope, and validate pipeline fixes, schema changes, ingestion rule improvements, and new data source integrations.
• Collaborate with Product to translate recurring data quality issues into platform enhancement requirements, preventive controls, and improved validation at the point of data capture.
• Represent data quality in global product and data governance forums — aligning India quality standards with Valocity's global policies and practices.
• Work with Business Development to provide data quality input into commercial proposals, new client onboarding assessments, and RFP responses.
• Communicate data quality status, risks, and improvement plans to senior leadership with clarity — translating technical findings into business language

 

 SKILLS & EXPERIENCE

1. 
   Essential Experience

• 5–8 years of experience in data quality, data management, data governance, or data operations — in BFSI, proptech, fintech, or enterprise SaaS platforms.
• Demonstrated experience designing and implementing data quality frameworks, scorecards, governance policies, and automated validation pipelines — not just running periodic checks.
• Experience working with complex, multi-source property, financial, or geospatial datasets at scale.
• Track record of leading data quality initiatives that resulted in measurable, documented improvement in data completeness, accuracy, or consistency.
• Experience working with external data providers — defining SLAs, conducting quality assessments, and managing provider-side data issues through to resolution.
• Experience collaborating with data engineering, product, and operations teams to drive root-cause resolution and systemic improvement — not just issue logging.
• Strong exposure to India's property data ecosystem is strongly preferred.

 

 

 

1. 
   Technical Skills

Skill Area

Expected Proficiency

SQL (Essential)

Strong — complex profiling queries, aggregations, window functions, anomaly detection, cross-source comparison

Python (Preferred)

Proficient — pandas, numpy, scripting for automated validation checks, data profiling, and pipeline quality testing

Excel / Power BI (Essential)

Advanced — pivot analysis, data modelling, executive dashboards, and quality scorecards for stakeholder reporting

Data Quality Tooling (Essential)

Hands-on experience with data quality frameworks

Azure / Cloud Platforms (Essential)

Working knowledge of Azure Data Factory, Azure Data Lake Storage, Azure Databricks, or equivalent cloud data infrastructure

Geospatial / Geocoding (Preferred)

Exposure to geocoding APIs, GIS tools, coordinate validation, and geospatial data quality assessment for property data

Jira / Confluence / DevOps (Preferred)

Comfortable using Jira for issue management, Confluence for documentation, and Azure DevOps or equivalent for delivery tracking

1. 
   Domain Knowledge

The candidate should have a strong understanding of:

• India's property data landscape — GV data by state, RERA project and unit data, SRO transaction records, property address fragmentation, and entity resolution challenges in the absence of a national property identifier.
• Data quality dimensions — completeness, accuracy, conformance, consistency, timeliness, and uniqueness — and how to measure, report, and systematically improve each.
• Master data management concepts — entity resolution, deduplication, canonical address matching, golden record creation, and reference data management.
• How data flows through a mortgage and property valuation and legal process — from lender instruction through field inspection, valuer report submission, legal report submission, review, approval, and analytics.
• Regulatory and data privacy considerations in India — DPDP Act 2023, RBI data localisation guidelines, and CERT-In requirements relevant to financial services platforms and understanding of how data quality standards support regulatory compliance and lender audit requirements.

1. 
   AI & Modern Tooling

• Practical, hands-on experience using AI tools (Claude, ChatGPT, Copilot, or equivalent) in day-to-day work — not just awareness.
• Understanding of how ML techniques — clustering, anomaly detection, fuzzy matching, NLP — can be applied to data quality and data governance problems at scale.
• Ability to define AI use cases for data quality and work with engineering teams to scope, evaluate, and prioritise feasibility.
• Awareness of AI risks in regulated data contexts — accuracy, explainability, hallucination, and privacy — particularly relevant to banking and mortgage data used for credit decisions.

PERSONAL ATTRIBUTES

The ideal candidate will be:

 

Attribute

What It Means for This Role

Data guardian mindset

Treats data quality as a non-negotiable standard — not a best-effort activity.

Strategically sharp

Can define a multi-year data quality roadmap and hold the line on standards even under delivery or commercial pressure.

Technically hands-on

Comfortable writing SQL, profiling datasets, and designing validation logic — not just managing reports from a distance.

Strong communicator

Translates complex data quality findings into clear business narratives for lenders, leadership, and engineering alike.

Commercially aware

Understands the downstream impact of data quality on lender trust, client retention, and Valocity's revenue and reputation.

Proactive and evidence-led

Raises data risks early, with supporting data, and a recommended course of action — does not wait to be asked.

Ownership through to resolution

Tracks issues from detection to confirmed fix — does not close a ticket until quality is re-verified.

Curious about data ecosystems

Interested in where data comes from, how it is governed, and what it means — across different states, sources, and regulatory regimes.

AI-curious and tool-forward

Actively looks for ways to automate, scale, and improve quality practices using modern tooling and AI — not attached to manual approaches.

Collaborative across teams

Works effectively with engineering, product, operations, and client-facing teams across multiple geographies and time zones.

Growth-oriented

Sees the India mandate as a foundation — and brings the appetite and curiosity to grow with Valocity into new markets over time.

 

EDUCATION & CERTIFICATIONS

 

• E., B.Tech, MCA, B.Sc. (Statistics / Mathematics / Computer Science), or equivalent technical qualification.
• Postgraduate qualification in Data Science, Analytics, Statistics, Information Management, or MBA is preferred.
• Certification or formal training in AI / ML fundamentals or data quality tooling (Great Expectations, dbt, Soda) is a strong plus.