• 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.

• Lead the design and delivery of enterprise AI applications, copilots, assistants, and agentic solutions. • Define implementation strategies that align AI capabilities with business objectives and user needs. • Guide teams in building scalable AI workflows, retrieval systems, orchestration layers, and tool-integrated experiences. • Drive best practices across prompt engineering, retrieval-augmented generation (RAG), tool calling, context management, testing, observability, and evaluation. • Partner with clients to understand business challenges and identify AI-powered opportunities. • Translate ambiguous requirements into practical implementation approaches and technical roadmaps. • Act as a trusted advisor during workshops, discovery sessions, and executive presentations. • Collaborate closely with Data Scientists, Data Engineers, Architects, Analytics Engineers, Product teams, and business stakeholders.

• Write production-level Python and SQL code, with a strong emphasis on complex SQL for data transformation, pipeline logic, and analytical query optimization. • Leverage AI tools and large language models to augment engineering workflows — from code generation and query optimization to automated testing, documentation, and process improvement. • Utilize AWS services such as Lambda, Glue, and Fargate for scalable data operations. • Build and maintain large-scale data pipelines using Apache Airflow. • Oversee the maintenance of Extract, Load, and Transform (ELT) processes using DBT. • Design optimized database schemas in PostgreSQL and Amazon Redshift to ensure data integrity and high performance. • Identify opportunities to apply AI-driven automation to repetitive or high-volume data engineering tasks, reducing manual effort and improving throughput.