• As a Data engineer, you will be responsible for designing, developing and operating our enterprise data platform. • Your role will involve ensuring that data is integrated, governed and AI-ready, thereby creating the backbone for all intelligent applications and insights across the business. • Design and own the enterprise data platform: Build a scalable Microsoft Fabric data infrastructure using a medallion lakehouse architecture (Bronze, Silver or Gold) with Delta Lake. • Develop and operate data pipelines: Create robust batch and streaming ETL/ELT pipelines with schema evolution, automated transformation and strong data quality validation. • Integrate enterprise systems: Connect to data sources such as SharePoint, Salesforce, Microsoft 365, Azure SQL, ERP/CRM systems, file repositories and REST APIs. • Ensure performance and reliability: Optimize storage and processing across structured and unstructured data, including monitoring, alerting, and operational stability. • Build semantic data layers & governance: Establish a taxonomy and metadata standards, as well as semantic models and data cataloguing and lineage tracking. • Enforce access control, compliance and security. • Drive AI readiness: Prepare data for AI use cases through document chunking, embedding pipelines, and vector-ready datasets for RAG. • Expose knowledge services & collaborate: Develop reusable APIs and data services for AI applications and work cross-functionally with AI, analytics, and business teams (#OneTeam).

• Design, build, and optimize end-to-end ETL pipelines for legal data • Work extensively with XML-based legal data feeds: parse, validate, normalize, and transform • Develop and maintain data models and storage schemas • Coordinate data handover and integration from multiple internal and external data providers • Implement and continuously refine metadata enrichment strategies • Build and maintain a high-performance search and retrieval infrastructure • Collaborate with product, AI, and legal domain experts to deliver high-quality data solutions • Own the data integration of one jurisdiction end-to-end

• Lead the most complex and high-impact data architecture initiatives across clients, business areas, domains, platforms, or strategic programs. • Define data architecture strategies that connect business goals, domain structures, semantic logic, platform realities, governance expectations, and downstream consumption needs. • Serve as a senior advisor to internal and client stakeholders on architecture maturity, semantic structure, governance direction, platform-aligned design, and long-term maintainability. • Establish and refine best practices for conceptual, logical, and physical data modeling, business entity design, semantic consistency, metric and dimension alignment, metadata expectations, domain boundaries, and reusable architecture patterns. • Guide the design of scalable semantic structures, business concept frameworks, taxonomy and ontology-informed models, governed access patterns, and reference architectures that improve trust and downstream usability. • Translate ambiguous executive and stakeholder questions into clear architecture approaches, semantic frameworks, platform strategies, governance-aligned design patterns, and business-relevant recommendations. • Lead architecture reviews and design authority discussions to identify risks, resolve ambiguity, strengthen standards alignment, and improve long-term structural quality. • Assess and shape how architecture choices support reporting, analytics, data products, machine learning, AI workflows, retrieval patterns, and agentic systems across structured, semi-structured, and selected unstructured data use cases. • Provide governance direction through standards, design reviews, architecture guardrails, and decision frameworks while keeping hands-on governance execution lighter than framework and review ownership. • Synthesize architecture tradeoffs, semantic implications, platform constraints, and governance considerations into clear insights, strategic implications, and recommended actions. • Influence cross-functional teams across DEPA, DSAI, and AIO to improve how data platforms, governed data products, semantic layers, analytics, and AI workflows work together. • Review major architecture deliverables to ensure quality, clarity, consistency, rigor, and practical business value. • Contribute to thought leadership, growth initiatives, proposal strategy, solution shaping, and new business efforts where senior architecture expertise is required. • Help create, improve, and promote reusable frameworks, templates, standards, semantic models, reference architectures, design review patterns, and accelerators that strengthen delivery consistency across the practice. • Mentor senior practitioners and help define what excellent data architecture practice looks like across the organization. • Reinforce strong governance, privacy, security, and data-quality expectations across engagements and teams.

• Lead complex data engineering workstreams across multiple business areas, source systems, domains, or stakeholder groups. • Define data engineering approaches that align business needs, source-system realities, platform constraints, transformation patterns, and downstream consumption requirements. • Translate ambiguous stakeholder needs into structured pipeline strategies, ingestion patterns, transformation designs, curated data layers, and actionable recommendations. • Guide the design and implementation of scalable ingestion, transformation, and serving patterns across cloud warehouses, lakehouses, and analytical environments. • Establish and reinforce best practices for schema design, pipeline modularity, layered data architecture, data validation, lineage awareness, scheduling discipline, error handling, and maintainable engineering patterns. • Design and improve reusable data engineering workflows that support reporting, dashboarding, data science, AI workflows, and agentic use cases. • Support team staffing, work allocation, prioritization, and delivery quality across data engineering engagements. • Manage, coach, and develop practitioners through feedback, guidance, and performance support. • Review deliverables for clarity, technical rigor, quality, consistency, and business usefulness. • Help teams improve data quality, reduce pipeline fragility, strengthen source alignment, and increase trust in downstream governed datasets. • Support data engineering patterns that improve AI and agentic readiness, including metadata-rich datasets, retrieval-supportive organization, document and chunk preparation support, governed access paths, and scalable data availability for downstream workflows. • Collaborate with Analytics Engineers, Data Analysts, Data Scientists, AI Scientists, AI Engineers, AI Platform Engineers, and Architects to align data foundations with downstream analytical, AI, and platform needs. • Contribute to hiring, onboarding, capability development, and team maturity within the data engineering practice. • Follow established governance, privacy, security, and data-quality standards across the work of the team.