Senior Data Governance & AI Governance Leader
Enterprise data, ready for trust, scale, and AI.
I lead Data Governance, AI Governance, and DataOps programs for regulated platforms — turning complex data operations into reliable, auditable, business-ready systems.
95%
Incident reduction
within 30 days
232M+
Records protected
per extraction cycle
$2M+
Annual cost saved
failure costs eliminated
50+
Enterprise clients
onboarded and governed
About
From data control to business confidence.
I help regulated enterprises turn fragmented data operations into trusted operating systems: governed, observable, audit-ready, and built for AI adoption. Across EY and FICO, I have led where engineering reliability, governance control, regulatory evidence, and business adoption meet.
At FICO I scaled data operations for 50+ enterprise clients, reduced production incidents by 95% in thirty days, protected 232M+ customer records per extraction cycle, eliminated $2M+ in recurring annual failure costs, and accelerated onboarding by 70% with metadata-driven quality controls.
I am targeting senior leadership roles in Data Governance, AI Governance, DataOps, and CDO Office leadership. I am based in Bengaluru, available immediately, DAMA CDMP Practitioner certified, and open to Director and Head of Data opportunities across the UAE, Saudi Arabia, Qatar, the broader GCC, APAC, and remote-first global teams.
Target Roles
Highest-alignment leadership roles
- Director / Head of Data Governance
- Director / Head of AI Governance
- Director / Head of DataOps
- Senior Manager / Director — Data Platform
- AI Governance & Controls Leader
- CDO Office / Data Strategy Lead
- Data Reliability Engineering Leader
Case Studies
Four proof points. One operating discipline.
01
Reliability, built into operations.
Context
Data operations across 50+ enterprise clients needed fewer escalations, faster ownership, and clearer root-cause evidence for every production incident.
Approach
Operationalised Detect → Resolve → Prevent with metadata validation, anomaly checks, ServiceNow routing, RCA-ready evidence, shift-left quality gates, and Defect Review Boards.
ServiceNow · AWS · Python · Boto3 · Databricks
Outcome
95% fewer incidents
within 30 days
4× faster resolution
mean time to resolution
$2M+ saved annually
02
Quality that scales without custom rework.
Context
Client onboarding depended on custom validation scripts, slowing delivery and making quality controls harder to reuse across schemas and formats.
Approach
Designed a metadata-driven DQ framework where validation rules lived in configuration, not code, with control files, checksums, and duplicate-file rejection embedded as platform standards.
AWS Athena · Python · Apache Hop · Metadata config
Outcome
70% faster onboarding
weeks to days
45% fewer escalations
downstream quality
Zero custom rework
03
Privacy controls at regulated scale.
Context
As AI/ML decisioning expanded across regulated financial platforms, privacy risk and audit exposure increased across ingestion, training sets, and downstream consumption.
Approach
Built a privacy-preserving governance layer with 100+ PII controls, KMS-backed masking, ABAC access control, end-to-end lineage, stewardship workflows, and audit-ready evidence.
AWS KMS · IAM · Databricks · Python · ServiceNow
Outcome
232M+ records protected
per extraction cycle
10,000 TPS throughput
at production scale
Zero privacy incidents
04
AI governance from model to evidence.
Context
AI/ML across credit decisioning and fraud detection required stronger explainability, model-risk controls, and regulatory transparency.
Approach
Designed model-lifecycle governance with SHAP and LIME explainability, drift monitoring, challenger/champion evidence, human review gates, privacy checks, and regulatory documentation.
Databricks · MLflow · SHAP · LIME · Python · AWS · ServiceNow
Outcome
50+ clients governed
governed at scale
Full audit trail
for regulatory submissions
Responsible AI by default
Domain Expertise
Eight domains. Fifteen years. Built in production.
Data Governance
Policy frameworks, stewardship workflows, DQ rules, business glossary, data ownership, audit evidence, and governance adoption across regulated enterprises.
AI / ML Governance
Model lifecycle governance, explainability controls, drift monitoring, challenger/champion tracking, privacy pre-training, and regulatory submission evidence.
DataOps & Reliability
Detect → Resolve → Prevent operating model. SLA management, automated incident routing, RCA evidence frameworks, and shift-left quality gates.
Data Quality
Metadata-driven rule frameworks, reconciliation controls, DQ scoring, shift-left validation, and reusable quality patterns deployed across 50+ enterprise clients.
MDM & Golden Record
Master data frameworks, deduplication, survivorship rules, Golden Record creation, entity resolution, and MDM adoption across regulated pipelines using Collibra and Microsoft Purview.
Metadata Management
Data catalogs, end-to-end lineage, business glossary, technical metadata classification, and metadata-driven automation across ingestion and consumption layers.
Privacy & Security
100+ PII controls, SHA-256/512 masking with KMS-backed salts, ABAC access control, GDPR and PDPL-aligned governance, and audit-ready evidence at scale.
Cloud & Platforms
15+ years across AWS, Databricks, ServiceNow, Apache Hop, and Python. ETL/ELT pipelines, batch and streaming ingestion, and enterprise BI consumption layers.
GCC & UAE
Built for regulated markets, ready for the Gulf.
The challenges I have spent 15 years solving map directly to what financial institutions, sovereign wealth funds, and national banks across the UAE and Saudi Arabia need now: trusted data, privacy control, regulatory evidence, and AI adoption that can stand up to scrutiny.
PDPL & Data Sovereignty
Regulatory-aligned governance
Saudi Arabia's PDPL and UAE privacy expectations require clear control of PII, residency, consent, and cross-border data movement. I have designed and operated these controls at enterprise scale across global regulated pipelines.
Regulated BFSI Depth
Every client was a regulated institution
Every client I served at FICO was a regulated financial institution: banks, insurers, and credit issuers with audit, lineage, access, evidence, and model-control expectations similar to UAE and KSA financial environments.
Cross-Border Delivery
Governance that works across jurisdictions
I have led data governance and operations teams across India, the US, and Canada, translating global frameworks into local controls while keeping quality, risk, and delivery aligned across regions.
Testimonials
What leaders who worked with me say.
"Ramachandran is a rare combination of strong technical skills, deep domain knowledge, and an exceptional ability to influence others. He is metric driven and extraordinarily accountable. He inspires teams to push boundaries and achieve ambitious goals."
Ken Bouley · Direct Manager at FICO · Executive Leadership
"Ram's enthusiasm and desire to find solutions were and are seriously impressive. Getting 7 SPOT awards at FICO must be some sort of record, and is a great reflection of how often Ram has gone out of his way and beyond the confines of his role to assist a wide array of people across the company and around the world."
"His work ethic and delivery was impeccable — one of the best. He demonstrated leadership qualities by building a framework and processes for all work in the domain of migrations and conversions — a robust framework re-used across all projects. I would rate him one of the finest data management professionals I have worked with."
Experience
Fifteen years. Larger scope. The same operating discipline.
Career Sabbatical
Family Caregiving & Professional Development
Apr 2025 – Apr 2026 1 year
- Planned sabbatical to support family caregiving; circumstances are fully resolved and I am immediately available.
- Stayed technically current across AI Governance, DataOps, Data Quality, RAG architectures, and responsible AI — built working prototypes to remain hands-on with current market practices.
- Progressed DAMA CDMP certification from Practitioner to Master level (approval pending); completed Scrum Master 2025, Data-Driven Decision Making 2025, and Anthropic AI courses.
FICO
Director / Senior Manager · AI Data Operations & Governance
Jun 2022 – Mar 2025 3 years
- Scaled Detect → Resolve → Prevent governance, reducing production incidents by 95% within three months and improving audit readiness through RCA, ServiceNow automation, and shift-left quality gates.
- Governed AI/ML models using SHAP, LIME, PII safeguards, and Databricks governance — protecting 125M+ regulated records with full explainability and audit-trail evidence for regulatory submissions.
- Mentored 11 direct reports and led a 25+ engineer organisation across 7 Agile teams spanning Canada, the US, and India.
FICO
Manager · Enterprise Data Platform, Data Operations & Governance
Dec 2018 – Jun 2022 3.5 years
- Automated metadata-driven data quality checks using AWS Athena, Python, and Boto3 — reducing incidents by 50% in three months and onboarding clients 70% faster with zero custom rework per client.
- Governed 75GB+ batch workloads and 2.5M–3M API calls per cycle with SLA-driven processing and reconciliation controls across 50+ enterprise clients.
- Embedded ingestion controls including control files, checksums, header/footer validation, and duplicate rejection — reducing downstream escalations by 45%.
FICO
Lead / Associate Manager · Data Management Platform
Oct 2016 – Dec 2018 2 years
- Built enterprise ingestion, validation, transformation, and reconciliation pipelines for decisioning and analytics platforms serving global financial clients.
- Delivered COBOL-to-JSON transformation for 3,500-column datasets, 25–35GB files, and 5M records within a 2-hour SLA.
- Led technical reviews, RFP inputs, and client demonstrations that contributed to 6+ new enterprise clients and $5M in new ARR.
HCL Technologies
Technical Lead · Data Migration, Reconciliation & Guidewire Integration
Apr 2015 – Oct 2016 1.5 years
- Spearheaded Guidewire Insurance Suite migrations for global teams using reusable ETL, validation, and reconciliation frameworks.
- Built validation, cleansing, and SQL optimisation workflows for BFSI and publishing clients.
EY · Cognizant · RR Donnelley
Associate Tech Lead / Programmer Analyst / Software Engineer
Jul 2009 – Mar 2015 6 years
- Delivered data migration, reporting, SQL automation, and enterprise application support across consulting and delivery roles.
- Progressed from software engineering into data management, reconciliation, and platform delivery leadership — the foundation for everything that followed at FICO.
Perspectives
Perspective shaped by production work.
Case Study · DataOps
Reducing enterprise data incidents by 95% in 30 days
The answer was not more tooling. It was a disciplined operating model — Detect, Resolve, Prevent — where every failure moved from symptom to ownership, evidence, root cause, and prevention.
Coming to LinkedInExecutive Perspective · AI Governance
AI governance is an operating model, not a document
Model cards matter, but they are not enough. BFSI teams need explainability, privacy checks, drift monitoring, human review, and evidence that stays current after deployment.
Coming to LinkedInRegional Insight · GCC
What GCC data programs need now
Saudi Arabia's PDPL and UAE governance expectations are turning data control into an operating requirement. Policies, stewardship, lineage, and evidence need to work in production, not only on paper.
Coming to LinkedInCredentials & Assets
Proof, credentials, and working assets in one place.
DAMA CDMP Practitioner
Certified Data Management Professional
Master-level approval in progress — expected within weeks. DAMA CDMP is the industry's most recognised certification for enterprise data management professionals.
Additional certifications
Contact
Let’s talk about the data outcomes you need next.
In 30 minutes, we can map your governance, AI, or DataOps challenge to the operating models I have built across FICO, EY, and regulated BFSI platforms.
What happens in 30 minutes
- You outline the governance, AI, or platform outcome you need
- I map it to directly relevant operating experience
- We assess fit against your team structure and seniority level
- You leave with a clear view of the value I can bring
Pick a time that works — no back-and-forth