Data Architecture

Tata Consultancy Services; Oslo; On-site; NOK 1,200,000 - 1,800,000; Full time

Job summary

Tata Consultancy Services in Oslo, Norway is seeking a Data Architect for Safety and Sustainability to lead the design and governance of a trusted, scalable, AI-ready data ecosystem spanning enterprise data, safety, ESG and regulatory domains.

You will shape target architecture, data models, governance, and AI-readiness capabilities, collaborating with IT, OT, engineering and regulatory teams to ensure lineage, provenance and risk management while delivering measurable business value.

Qualifications

• 10+ years in enterprise data architecture, governance and analytics.
• Oil & Gas, Energy, Utilities, Mining or safety-intensive sector experience desirable.
• Experience across enterprise IT and operational/engineering data environments.

Responsibilities

• Design secure, scalable enterprise data ecosystems across operational, analytical and AI workloads.
• Define target architecture, data models and reference patterns aligned to business needs.
• Architect cloud-native data lake, lakehouse, data mesh and metadata capabilities with governance.

Skills

Data architecture

Data governance

AI readiness

Cloud platforms

SQL & data modeling

Knowledge graphs

OT/IT integration

MLOps

Tools

Azure

Databricks

Snowflake

Job description

Role: Data Architecture for Safety and Sustainability

Location: Oslo, Norway

Employment type: Full-time

Experience required: 10+yrs

About role:

Lead the design and governance of a trusted, scalable and AI-ready data ecosystem for safety and sustainability, combining enterprise architecture, Oil & Gas domain expertise, governance and responsible AI.

Key Mandate: Own the Data Readiness for AI framework, with measurable standards for quality, metadata, semantics, lineage, governance and AI trustworthiness, while applying AI to improve these dimensions at scale.

1. Data Strategy & Architecture

• Design secure, scalable and cost-efficient enterprise data ecosystems spanning operational, analytical and AI workloads.
• Define target architecture, reference models, reusable patterns and conceptual, logical and physical data models.
• Architect cloud-native lakes, lakehouses, data mesh, streaming and metadata capabilities, aligned to business priorities and future needs.

2. AI Readiness & AI-Driven Data Engineering

• Create AI-ready foundations using rich metadata, semantic models, knowledge graphs, lineage, governance and retrieval mechanisms.
• Use AI for discovery, metadata enrichment, schema mapping, classification, master-data harmonization, quality remediation and lineage generation.
• Enable RAG, vector databases, semantic search and grounded AI with provenance, explainability, auditability and appropriate human oversight.

3. Safety & Sustainability Data Architecture

• Unify HSE, ESG, process safety, asset integrity, operational and chemical risk, incidents and regulatory compliance data.
• Integrate IT, OT, engineering, sensor, maintenance and environmental systems through a common information model.
• Preserve safety-critical context such as units, asset/location hierarchy, timestamps, provenance, confidence and business rules; enable trusted near-real-time insight and reporting.

4. Data Governance & Compliance

• Establish lifecycle governance covering ownership, stewardship, quality, retention, privacy, security and regulatory compliance.
• Implement catalogue, glossary, metadata, lineage and policy controls for structured and unstructured data.
• Translate legal, risk, cyber, HSE and sustainability obligations into enforceable controls.
• Design integration across ERP, EAM, CMMS, OT, GIS, IoT, ESG, safety, engineering and external regulatory systems.
• Define batch, streaming, event, API and federated patterns for structured, semi-structured and unstructured data.
• Create canonical models, semantic mappings and identity resolution for assets, equipment, locations, people, chemicals, incidents and environmental observations.

6. Data Quality, Observability & Trust

• Operationalize profiling, validation, monitoring, issue management and remediation across critical data products.
• Monitor freshness, completeness, accuracy, consistency, reliability and SLAs using measurable scorecards and trust indicators.
• Detect anomalies, drift, duplication, broken lineage and source changes with clear ownership and escalation.
• Define data-product ownership, contracts, service levels, quality measures, discoverability and consumption models.
• Enable federated governance, self-service analytics and AI through reusable, governed and documented data products.
• Set acceptance criteria for fitness for purpose, security, lineage, metadata, quality, support and deprecation.

8. Security & Risk Management

• Apply security by design: encryption, masking, tokenization, least privilege, role/attribute-based controls and zero-trust access.
• Partner with cybersecurity to protect operational, environmental, workforce and regulatory data.
• Assess architectural risk and enforce segregation, consent, retention, residency and audit controls for data and AI-enabled processes.

9. Leadership & Stakeholder Engagement

• Advise business, product, HSE, sustainability, data science, AI and engineering leaders on enterprise data strategy and investment.
• Align safety and sustainability outcomes with data architecture, delivery priorities and measurable business value.
• Champion architectural consistency and governance; collaborate across teams and anticipate evolving needs and technologies.
• Mentor architects, engineers and stewards, fostering collaboration, innovation, stewardship, data literacy and responsible AI.
• Target-state architecture, principles, standards, decision records and implementation roadmap.
• Priority domain models, integration patterns, governed data-product specifications and AI-readiness of the data elements.
• Architecture assurance, risk management and traceability from business outcomes to data and platform capabilities.

Domain Experience:

• 10+ years in enterprise data architecture, platforms, integration, governance and analytics, including 5+ years in Oil & Gas, Energy, Utilities, Mining, Chemicals or a comparable safety-intensive industry.
• Strong knowledge of safety and risk, sustainability/ESG, asset integrity, process safety and risk management, environmental monitoring, operational excellence or HSE.
• Experience across enterprise IT and operational/engineering data environments.

Technical Expertise:

• Major cloud and modern data platforms such as Azure, Microsoft Fabric, Synapse, Databricks, Snowflake.
• Strong SQL, data modelling, integration and distributed processing; metadata, catalogue, glossary, lineage, governance and observability tooling.
• Knowledge graphs, vector databases, semantic layers, RAG and AI-enabled data architecture; enterprise engineering and appreciation of OT/IT integration.
• API/event architecture, DevSecOps, DataOps and MLOps; knowledge of OSDU or industrial data-platform familiarity is desirable.

Strategic & Leadership Skills:

• Define data strategy, target architecture, roadmaps, investment priorities and operating models; translate AI use cases and applicability into data requirements.
• Communicate complex architecture clearly to executives, domain specialists and engineering teams.
• Lead large-scale transformation, facilitate decisions, manage stakeholders and conflict, mentor teams and balance strategy with pragmatic delivery.

Why Join Our Team?

• Innovative Projects: Work on high-impact projects that drive business insights and transformation.
• Professional Growth: We encourage learning and development, with opportunities to deepen your expertise knowledge.
• Collaborative Culture: Join a team that values collaboration, innovation, and the power of data.

ABOUT US:

Tata Consultancy Services is a digital transformation and technology partner of choice for industry-leading organizations worldwide. Since its inception in 1968, TCS has upheld the highest standards of innovation, engineering excellence and customer service.

Rooted in the heritage of the Tata Group, TCS is focused on creating long term value for its clients, its investors, its employees, and the community at large. With a highly skilled workforce of over 607,000 consultants in 55 countries and 180 service delivery centers across the world, the company has been recognized as a top employer in six continents. With the ability to rapidly apply and scale new technologies, the company has built long term partnerships with its clients – helping them emerge as perpetually adaptive enterprises. Many of these relationships have endured into decades and navigated every technology cycle, from mainframes in the 1970s to Artificial Intelligence today.

TCS sponsors 14 of the world’s most prestigious marathons and endurance events, including TCS Lidingöloppet, TCS London Marathon and TCS Amsterdam Marathon with a focus on promoting health, sustainability, and community empowerment. TCS generated consolidated revenues of over US $30 billion in the fiscal year ended March 31, 2025.

With a history of over 45 years in Europe, TCS is a transformation partner for companies in industries that range from banking and retail to insurance and travel. Our agile and diverse workforce is distributed in 62 offices across Europe.

• TCS has been ranked number 1 in Customer Satisfaction in Europe for 12 consecutive years.
• TCS has been named Top Employer in Europe for 13 consecutive years
• TCS has been recognized as one of the world’s Top 50 Brands by Kantar BrandZ in 2025
• 80,000 associates are supporting clients in Europe.

For more information, visit www.tcs.com

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