Job Title: Data Architect / Governance Lead

Location: Ahmedabad, Gujarat / Remote
Job Type: Full-Time
Department: Data & Analytics

About Simform

Simform is a premier digital engineering company specializing in Cloud, Data, AI/ML, and Experience Engineering to create seamless digital experiences and scalable products. Simform is a strong partner of Microsoft, AWS, Google Cloud, and Databricks, with a global presence and a strong focus on delivering high-quality engineering solutions for clients across North America, the UK, and Northern Europe.

Role Overview

We are looking for a Data Architect / Governance Lead to provide strategic leadership and hands-on implementation for enterprise Data & AI Governance initiatives, particularly within financial services and lending environments.

The role will own the governance operating model across Gold data products, semantic metrics, Master Data Management (MDM), ontology, BI, Machine Learning, and AI assets.

This role combines enterprise-level decision-making authority with hands-on technical implementation. The successful candidate will define governance policies, ownership models, certification standards, security controls, metadata frameworks, and approval processes while also working directly with platforms such as Atlan, Snowflake, dbt, Dagster, SQL, Python, and Git-based CI/CD.

The role will ensure governance is embedded into the creation, promotion, and consumption of data and AI assets rather than being treated as a post-delivery review process.

Key Responsibilities

Data & AI Governance Strategy

• Define and operationalize the enterprise Data & AI Governance framework across data products, analytics, BI, ML, and AI.

• Translate the Data & AI operating model into a practical governance charter, RACI, decision rights, governance forums, escalation mechanisms, and operating procedures.

• Establish governance policies covering:

  • Critical Data Elements (CDEs)

  • Data sensitivity

  • Persona classification

  • Data quality tiers

  • Regulatory and retention requirements

  • Business-product classification

  • Decision-intent classification

• Establish governance standards for data products, semantic metrics, dashboards, ML models, and AI assets.

• Define policies for certification, de-certification, change management, exception handling, evidence retention, and approval workflows.

• Establish separation-of-duties requirements and appropriate governance controls for critical data and analytics assets.

• Lead governance councils, executive KPI reviews, control-violation escalations, and governance decision forums.

• Provide final governance sign-off for assets and initiatives falling within defined governance scope.

Data Product & Asset Governance

• Establish certification policies and lifecycle management for:

  • Gold Core Data Products

  • Domain Data Products

  • Consumer Serving Models

  • Semantic Metrics

  • BI Dashboards

  • ML Models

  • AI Assets

• Ensure Tier 0 and other critical assets have clearly assigned owners and stewards.

• Establish mandatory metadata, lineage, certification, and ownership requirements.

• Control duplication of data products, metrics, and analytical assets across the enterprise.

• Define and implement governance gates to prevent production promotion without required approvals and evidence.

• Establish asset-level change controls and assess potential downstream impact or blast radius before significant changes.

• Ensure governance evidence is attributable, reproducible, and audit-ready.

Data Catalog & Metadata Governance

• Configure and manage Atlan domains, data products, glossary, ownership, classifications, custom metadata, lineage, and certification lifecycle states.

• Develop and maintain an enterprise business glossary, ontology, controlled taxonomies, and metadata standards.

• Establish ownership and stewardship registries across data products and business domains.

• Maintain structured registries for:

  • Data products

  • Personas

  • Metrics

  • Regulatory and retention rules

  • Approval policies

  • Business definitions

• Ensure metadata is consistently captured and propagated across the data ecosystem.

• Integrate governance metadata with data engineering and analytics workflows.

• Establish catalog synchronization and metadata quality controls.

Data Security & Access Governance

• Design and review Snowflake data governance and security controls.

• Work with:

  • Snowflake tags

  • Masking policies

  • RBAC / ABAC

  • Row-level access controls

  • Column-level access controls

  • Audit logging

  • Metadata propagation

• Ensure sensitive and regulated data is appropriately classified and protected.

• Define governance requirements for persona-based access and controlled consumption of data products.

• Review access models and security controls for alignment with enterprise governance policies.

• Establish appropriate audit and monitoring mechanisms for governed data assets.

Governance Automation & Policy Enforcement

• Build and review publish-time and query-time governance policy gates.

• Implement completeness checks, approval workflows, catalog synchronization, and evidence-generation mechanisms.

• Develop governance automation using:

  • SQL

  • Python

  • pyatlan

  • YAML / JSON

  • Git-based CI/CD

• Integrate governance controls with data pipelines and orchestration frameworks.

• Automate lineage capture, certification evidence, audit exports, and governance reporting.

• Work with engineering teams to embed governance controls directly into data and analytics delivery pipelines.

Data Quality & Semantic Governance

• Establish data quality governance standards across enterprise data products.

• Define data quality tiers, ownership, thresholds, monitoring expectations, and escalation mechanisms.

• Integrate governance requirements with dbt tests and semantic assets.

• Establish governance processes for enterprise metrics and semantic definitions.

• Maintain a controlled Metric Registry and ensure approved metrics are consistently consumed across BI, analytics, ML, and AI use cases.

• Prevent unauthorized duplication or modification of enterprise KPI and metric definitions.

MDM & Ontology Governance

• Design and govern Master Data Management (MDM) stewardship processes.

• Establish controls around:

  • Match

  • Merge

  • Split

  • Override

  • Exception handling

• Define decision ledgers and evidence requirements for MDM decisions.

• Establish SLAs, ownership, approval workflows, and segregation-of-duties controls for master data stewardship.

• Develop and maintain enterprise ontology and controlled business taxonomies.

• Partner with business and technical teams to ensure consistent master data definitions and usage.

Financial Services & Regulatory Governance

• Establish governance practices appropriate for financial services and lending data environments.

• Work with stakeholders to ensure data classification, retention, access, lineage, and auditability support applicable regulatory requirements.

• Maintain working awareness of relevant requirements including:

  • Reg B / ECOA

  • FCRA

  • GLBA

  • BSA / AML

  • Model Risk Management

  • Audit and evidence requirements

• Partner with risk, compliance, legal, security, and business stakeholders where governance decisions have regulatory implications.

• Ensure governed assets maintain appropriate evidence and traceability for audits and regulatory reviews.

Stakeholder & Executive Management

• Lead governance discussions with senior business, technology, data, risk, compliance, and executive stakeholders.

• Challenge architectural and engineering decisions where they conflict with governance, security, quality, or regulatory requirements.

• Communicate complex governance decisions in a clear and business-oriented manner.

• Establish governance KPIs and executive reporting mechanisms.

• Lead control-violation escalation and remediation tracking.

• Develop role-based governance training and enablement material.

• Promote a governance culture where accountability and ownership are clearly defined.

Technical Responsibilities

• Hands-on configuration and governance implementation using Atlan or equivalent data catalog platforms.

• Strong working knowledge of Snowflake, SQL, metadata, lineage, and access-control mechanisms.

• Implement and review Snowflake:

  • Tags

  • Masking

  • RBAC / ABAC

  • Row-level security

  • Column-level security

  • Audit logging

• Develop governance automation using Python / pyatlan and configuration formats such as YAML and JSON.

• Work with Git-based CI/CD for governance configuration and controlled changes.

• Integrate governance with dbt tests and semantic models.

• Work with Dagster or equivalent orchestration frameworks to embed governance controls into data workflows.

• Design automated lineage, certification, metadata synchronization, evidence-generation, and audit-export processes.

• Maintain governance configuration repositories, control matrices, evidence tables, and operational documentation.

Required Skills & Qualifications

• 10–14 years of experience in Data, Analytics, Data Architecture, or Data Governance.

• 5+ years of experience leading enterprise data governance, metadata, or data management programs.

• 3+ years of hands-on experience implementing data catalog and governance platforms.

• Strong hands-on experience with Atlan or equivalent platforms such as Collibra or Alation.

• Strong knowledge of Snowflake, SQL, metadata, lineage, data quality, and data governance.

• Strong understanding of MDM, ontology, semantic metrics, and data product governance.

• Experience designing governance operating models, policies, approval workflows, and certification frameworks.

• Experience implementing data security and access governance using Snowflake capabilities.

• Strong understanding of data classification, ownership, stewardship, lineage, certification, and auditability.

• Experience working with engineering teams to embed governance controls into CI/CD and data pipelines.

• Strong stakeholder management and executive communication skills.

• Ability to make policy decisions while remaining technically hands-on with governance platforms and configuration repositories.

Good to Have

• Experience working within financial services, banking, lending, risk, underwriting, or regulated data environments.

• Hands-on experience with Atlan APIs / pyatlan.

• Experience with dbt, MetricFlow, or equivalent semantic-layer technologies.

• Experience with Dagster or similar orchestration platforms.

• Experience implementing enterprise MDM and ontology programs.

• Experience with data contracts, data products, and modern data mesh/data platform concepts.

• Experience with AI/ML governance and model-risk frameworks.

• Experience with enterprise data catalogs such as Collibra or Alation.

• Strong understanding of regulatory and audit requirements for financial-services data.

Preferred Certifications

• Atlan certification or equivalent data governance/catalog certification.

• SnowPro certification.

• DAMA / CDMP.

• DCAM or equivalent data management/governance certification.

Certifications are preferred; demonstrable hands-on implementation experience and governance leadership depth are more important.

Key Competencies

• Strategic data governance leadership.

• Strong Data Architecture understanding.

• Hands-on governance implementation capability.

• Strong Snowflake and SQL expertise.

• Metadata, lineage, catalog, and semantic governance expertise.

• Strong MDM and ontology understanding.

• Data quality and certification governance.

• Security and access-control knowledge.

• Strong regulatory and audit awareness.

• Executive stakeholder management.

• Strong decision-making and problem-solving abilities.

• Ability to challenge technical and business stakeholders constructively.

• Ability to balance strategic governance requirements with practical engineering implementation.

Expected Outcomes

• A clearly defined and operational Data & AI Governance framework with ownership, decision rights, RACI, policies, and escalation mechanisms.

• A maintained enterprise business glossary, ontology, taxonomy, metadata framework, and stewardship registry.

• Controlled certification and de-certification lifecycle for critical data, BI, ML, and AI assets.

• Strong governance enforcement at data creation, promotion, and consumption stages.

• Accurate ownership, lineage, classification, certification, and metadata for critical enterprise assets.

• Effective Snowflake security, access, masking, and audit controls.

• Automated governance checks, approval workflows, evidence generation, and audit exports.

• Controlled enterprise metric and decision-intent registries.

• Effective MDM stewardship and decision controls.

• Audit-ready governance evidence and reporting.

• Reduced duplication and uncontrolled creation of enterprise data and analytical assets.

A governance framework that is practical, enforceable, measurable, and embedded into the engineering lifecycle.



Why Join Us:

• Young Team, Thriving Culture

• Flat-hierarchical, friendly, engineering-oriented, and growth-focused culture.

• Well-balanced learning and growth opportunities

• Free health insurance.

• Office facilities with a game zone, in-office kitchen with affordable lunch service, and free snacks.

• Sponsorship for certifications/events and library service.

• Flexible work timing, leaves for life events, WFH and hybrid options