Technology Manager - Data Ecosystem

Technology Manager - Data Ecosystem

The Technology Manager is central to business-critical transformation of technology delivery and achieving Sainsbury's business strategy, working at the heart of a complex matrix operating model spanning internal teams, offshore partners, and strategic technology organisations.

Responsible for assuring technology service and delivery provision for Data Engineering, managing the performance of external partners providing engineering and BAU services, and driving financial accountability for the data domain.

This role business partners at senior level, ensures fit-for-purpose, secure, efficient data products and applications, and drives continuous improvement in technical processes to deliver value early and often while maximising value for money across contracts and services.

What I am accountable for

• Data Strategy
• Contribute to data strategies for respective products and functional areas, working closely with Data Architecture, Engineering Teams, and Product Management to ensure alignment with business objectives and data technology principles.
• Partner with Product Managers and Data Science teams to ensure delivery of data engineering services that meet analytical, reporting, and ML requirements, balancing business needs with technical feasibility and operational constraints.
• Ensure data pipelines are delivered in accordance with Sainsbury's Tech guidelines, data governance standards, and technology principles, evolving these based on requirements and learnings from delivery.
• Input into overall data roadmaps, ensuring data solutions fit with business strategy and agenda and that all initiatives have clear and appropriate action plans covering data ingestion, transformation, quality, and access.
• Leadership
• Drive a culture of personal accountability and ownership across direct contributions and external offshore Data Engineering teams, ensuring high performance, data quality standards, and delivery excellence.
• Coach offshore data engineering teams to understand the rationale for technical approaches in data architecture and pipeline design, surfacing and dealing with ambiguity and conflicting demands, escalating appropriately to resolve prioritization conflicts.
• Build collaborative relationships with Data Scientists, Data Analysts, Business Intelligence teams, service providers, suppliers, and wider Sainsbury's Tech colleagues to ensure data services meet required standards and expectations.
• Engage and influence at all levels, partnering with data teams and business stakeholders to maintain and evolve a strong service mindset focused on data quality, accessibility, and business value.
• Influence brilliantly through timely communication of relevant information up to Director level, translating complex data engineering and technical issues to meet the demands of diverse audiences.
• Make decisions with ambiguous or incomplete information, exercising judgment in the absence of clear guidelines and frameworks while managing data-related risks appropriately.
• Financial Accountability
• Manage small budget, ensuring financial discipline across both change and run activities for data platforms, tracking spend against forecasts and business cases.
• Support Procurement and Supplier Management on supplier selection processes for data engineering partners, establishing and managing ongoing relationships with selected suppliers and ensuring offshore Data Engineering teams deliver against third-party obligations.
• Intervene and mitigate potential financial or contractual issues with data engineering partners, ensuring commercial risks are identified early and managed proactively with appropriate stakeholder involvement.
• Technical Assurance
• Work alongside Product, Data Architecture, Data Engineering, and third-party suppliers to identify key risks and issues early in delivery, building sensible mitigation approaches and securing stakeholder support where necessary.
• Act as key point of contact for Data Engineering, BAU, third-party managed data solutions, and vendors, ensuring clear communication and coordination across the data engineering domain.
• Engage with data engineering technical detail when needed, understanding the data platform landscape including data ingestion, transformation (DBT), orchestration (Airflow), streaming (Kafka), and data warehousing (Snowflake), and able to articulate technical choices and trade-offs to different stakeholders.
• Work at both conceptual and detailed levels in data architecture and engineering, prepared to get into technical and commercial specifics while maintaining strategic perspective on data service delivery.
• Ensure data quality and governance standards are maintained across offshore delivery, implementing appropriate testing, validation, and quality assurance processes for data pipelines and platforms.
• Delivery Assurance
• Culturally embed a methodology for delivering high-quality data engineering services across the relevant domain, ensuring consistent approaches, standards, and data quality practices.
• Use sound judgment to focus on the most critical and impactful data initiatives, making best use of available offshore and internal resources and prioritising effectively across competing demands.
• Review acceptance of data solutions into BAU support, ensuring appropriate readiness, data quality validation, documentation, and operational capability before transition.
• Approve changes to data platforms and pipelines in the live environment, balancing business need with risk management, data integrity, and operational stability.
• Feed into data rollout and deployment plans, ensuring practical, achievable approaches that minimize risk to data quality and maximize successful adoption.
• Service and Risk Management
• Ensure assurance of day-to-day running of data engineering services within the Data division and associated operational activities.
• Conduct regular Service Reviews with offshore data engineering suppliers, driving outputs and actions to closure, holding partners accountable for performance, data quality, and improvement.
• Support Service Transition processes for new data solutions including knowledge article generation, runbook documentation, and ensuring smooth handover from delivery to operations for data pipelines and
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