• Define and implement end-to-end Data Lakehouse solutions on AWS. • Lead the automation of cloud infrastructure using Terraform. • Orchestrate large-scale performance tuning initiatives. • Establish automated Data Quality gates using AWS Glue Data Quality. • Design complex, event-driven workflows using Step Functions and Airflow. • Serve as the primary technical liaison between Data Science, BI teams, and Business Stakeholders.

• Lead a distributed engineering team across platform/product engineering, connectors, QA, DevOps/infra, AI implementation, data governance, and support. • Own delivery, quality, release discipline, and execution of the technical roadmap. • Install engineering discipline where it's thin: automated testing, QA, CI/CD, release governance, and versioning. • Drive repeatable, perimeter-safe deployments, including containerization, infrastructure-as-code, secure deployment, and SOC 2 readiness. • Partner with the Chief Architect on the connector framework, canonical metadata model, architecture decisions, and product IP. • Build the team: assess current talent, retain the strong, hire the gaps, and align the team plan to the roadmap. • Push practical AI use across coding, review, testing, ops, and engineering productivity.

• Own the end-to-end product design vision for a data-dense enterprise platform. • Translate complex lineage, migration impact, governance, and blast-radius workflows into clear, navigable product experiences. • Partner closely with Product and Engineering to define what gets built, for whom, and why. • Design for two audiences at once: technical users who need depth and senior stakeholders who need confidence in decisions. • Build and maintain the design system, set the quality bar, and create repeatable standards for future product surfaces. • Run user discovery and research; turn what you learn into product direction, design decisions, and prioritization input. • Use AI across the design workflow, including research synthesis, ideation, prototyping, iteration, and productivity.

• Define the multi-year vision for the Data Engineering Practice, ensuring our technical capabilities are ahead of the curve for enterprise demand for Data & AI transformation. • Own the full P&L, including pipeline, pricing, delivery margin, and revenue growth, while reporting directly to executive leadership with clear commercial accountability. • Build and sustain senior relationships with Google Cloud partner teams and client executives to generate meaningful deal flow and expand strategic accounts. • Lead the practice's position on ethical AI and data democratisation while making principled, evidence-based bets on where advanced analytics and AI are creating real enterprise value. • Drive investment in reusable IP, data accelerators, and delivery assets that improve consistency, reduce time-to-value, and protect margins at scale. • Serve as a strategic advisor to client C-suite executives, helping them define data strategy, navigate AI adoption, and build the organisational capabilities to sustain it. • Set the standard for engineering and analytical excellence across the practice by hiring well, developing talent deliberately, and building teams that clients trust and return to.