• 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.

• Design, develop, and maintain scalable ETL/ELT pipelines to process large volumes of data from diverse sources. • Build and optimize data storage solutions — data lakes and data warehouses — for efficient retrieval and processing. • Integrate structured and unstructured data from internal and external systems into a unified view for analysis. • Ensure data accuracy, consistency, and completeness through validation, cleansing, and transformation. • Maintain clear documentation for data processes, tools, and systems. • Build and maintain Tableau dashboards and reports that translate complex datasets into clear, decision-ready visuals. • Design data models and extracts optimized for Tableau performance, including live connections and published data sources. • Apply data visualization best practices — chart selection, layout, color, and interactivity — to produce client-ready output. • Partner with stakeholders to understand reporting needs and translate them into visual solutions. • Support ad hoc analysis using Tableau, Python-based charting (matplotlib, seaborn, plotly), or similar tools. • Support AI/ML workflows by building and maintaining the data pipelines that feed model training, inference, and evaluation. • Assist with data preparation for LLM and machine learning projects, including feature engineering, tokenization pipelines, and vector store integration. • Help teams adopt AI-assisted data tooling — copilots, intelligent search, automated reporting — by ensuring clean, well-structured data is available upstream. • Contribute to prompt engineering and evaluation frameworks where data context is a key input. • Work with product managers and stakeholders to gather requirements and translate them into technical solutions. • Provide technical input during requirements sessions to align data capabilities with business needs. • Participate in sprint planning, stand-ups, and sprint reviews. • Deliver solutions on time and within scope. Adapt when priorities shift. • Write unit and integration tests to validate pipeline reliability and data accuracy. • Identify and resolve defects, performance bottlenecks, and data quality issues. • Stay current with cloud platforms (AWS, Azure, GCP) and emerging data engineering tools. • Propose solutions to improve performance, security, and scalability.