You will build trusted data and trusted AI - ensuring our clients data is accurate, compliant, and governed, and our ML models are reproducible, monitored, and responsibly deployed to production.
This role is 50% Data Governance, 50% MLOps / ML Platform Governance.
Key Responsibilities
A. Data Governance (50%)
- Framework & Stewardship
- Design and run enterprise Data Governance framework, policies, and RACI for data owners/stewards
- Establish Data Governance Council and operating model across Product, Engineering, Analytics, and Business
- Define KPIs: catalog coverage, data quality score, policy adherence
- Data Quality, Catalog & Lineage
- Implement business glossary, data catalog (Collibra / Alation / Purview / DataHub), and end-to-end lineage
- Define and monitor data quality rules, SLAs, anomaly detection for critical domains (Customer, Product, Transaction)
- Manage data classification, PII/PHI tagging, retention, and access control policies
- Compliance & Security
- Ensure compliance with PDPA, GDPR, CCPA and internal security standards
- Partner with DPO / Legal / GRC for consent, purpose limitation, anonymization, and audit readiness
- Own access governance - RBAC/ABAC for data warehouse, lakehouse, and feature store
B. MLOps & AI Governance (50%)
- ML Lifecycle & Platform
- Own MLOps best practices: from feature engineering -> training -> validation -> deployment -> monitoring
- Build and manage ML platform components: Feature Store (Feast / Tecton / SageMaker Feature Store), Model Registry (MLflow / SageMaker Model Registry), Experiment Tracking
- Standardize CI/CD/CT for ML with Git, Docker, Airflow / Kubeflow / SageMaker Pipelines
- Model Governance & Responsible AI
- Implement Model Governance: model inventory, model cards, lineage (data -> features -> model -> endpoint), approval workflows
- Enforce responsible AI checks: bias/fairness, explainability, drift, and reproducibility
- Align with AI Governance frameworks: NIST AI RMF, Singapore Model AI Governance Framework, AI Verify, ISO 42001
- Monitoring & Operations
- Implement monitoring for data drift, concept drift, feature skew, and model performance degradation
- Set up alerting, automated retraining triggers, and rollback strategies
- Optimize model serving costs, latency, and scalability on AWS / Azure / GCP
Tech Stack You Will Work With
Governance: Collibra, Alation, Purview, Informatica, DataHub, AWS Glue, Apache Atlas
Data: Snowflake / BigQuery / Redshift, S3 / GCS, dbt, Airflow, Spark, Kafka
MLOps: MLflow, Kubeflow, SageMaker, Vertex AI, Feast, Evidently, Great Expectations, Docker, Kubernetes, GitHub Actions
Languages: Python (must), SQL (must), PySpark
Requirements
- 6-10 years total in Data Engineering / Data Governance / MLOps
- At least 2+ years owning data governance and at least 2+ years deploying ML models to production
- Strong hands-on with DAMA-DMBOK and MLOps principles
- Proven experience setting up Model Registry, Feature Store, and monitoring for production ML systems
- Deep understanding of PDPA/GDPR, data security, and AI risk
- Excellent stakeholder management - you can talk to both Data Scientists and Risk/Legal
Nice-to-Have
- CDMP, AWS Certified ML Specialty, or similar
- Experience with LLM / GenAI governance - prompt logging, RAG governance, hallucination monitoring
- Experience with Great Expectations, Monte Carlo, Evidently AI
- Industry experience in Media, FinTech, or other regulated industry
What Success Looks Like in 12 Months
- Top 5 data domains governed with SLAs and quality monitoring >95%
- 100% of production models registered with model cards, lineage, and approval workflow
- Automated drift detection live for all critical models with <2hr alert SLA
- Data catalog adoption >80% and zero compliance audit findings