We are seeking a Senior Data Engineering Manager to lead a small, high-performing data engineering team delivering scalable, reliable, and cost-effective data staging and analytics solutions across the enterprise data ecosystem. This role owns the operational reliability, performance, and continuous improvement of data platforms that power business intelligence, reporting, and emerging advanced analytics use cases. About the Role This is a hands-on leadership role: you will coach and uplift a team with mixed experience levels, introduce stronger engineering standards and operating procedures, and help the organisation reduce waste (e.g., avoidable platform spend) through better design and disciplined delivery. Our environment includes Snowflake for analytics aggregation, alongside row-based platforms such as Postgres and cloud services (Azure/AWS), plus enterprise integration tooling (e.g., Talend) where applicable. Responsibilities Lead, mentor, and develop a small team of data engineers (currently ~3 people, growing). Establish clear delivery processes (intake, estimation, backlog management, sprint planning, release coordination). Drive predictable execution, measurable outcomes, and continuous improvement. Define and track delivery KPIs (reliability, cycle time, defect rates, platform stability). Oversee operational support, incident management, and structured root cause analysis for data pipelines. Create a healthy team dynamic by coaching, supporting, and developing junior engineers. Own structured intake and estimation processes for new data initiatives. Partner with stakeholders to prioritise work based on business value, risk, and capacity. Manage team workload, resource allocation, and delivery timelines; surface trade-offs early. Provide transparent status reporting and maintain alignment with enterprise data strategy. Deliver curated datasets and staging solutions across Snowflake (centralised analytics and aggregation layers), Postgres, and cloud data services (Azure/AWS). Design and implement robust ETL/ELT pipelines for scalable, reliable ingestion and transformation. Implement version control, automated testing, and CI/CD practices for data pipelines. Optimise workload placement to balance cost, performance, scalability, and maintainability. Deliver structured, analytics-ready data for BI/reporting consumers and enable future advanced analytics. Design pipelines that support semi-structured data and reproducible patterns suitable for ML/AI needs as they arise (not an AI engineering role). Optimisation, Observability & Governance Promote standard pipeline patterns, reusable components, and shared curated datasets to reduce duplication. Drive cost awareness and optimisation across compute, storage, and transformation layers (e.g., reduce avoidable Snowflake spend through better design). Implement data quality monitoring, observability, lineage, and measurable SLAs for critical datasets. Partner with Enterprise Data Architecture to align engineering deliverables with target-state models, standards, and governance. Maintain strong documentation practices (architectural decisions, runbooks, operating procedures). Qualifications Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent practical experience. 8+ years’ experience in data engineering, data warehousing, or analytics engineering with ownership of production-grade data platforms. 3+ years’ experience leading or managing a small engineering team. Strong SQL skills and hands-on experience building and operating production data pipelines. Experience designing and operating modern ETL/ELT pipelines using tools such as Talend (or comparable platforms). Working experience with Snowflake or similar cloud data warehouse platforms. Experience with Postgres (or comparable relational databases) and sound judgement on workload placement decisions. Experience implementing data testing, monitoring, and observability practices. Comfortable using AI-assisted engineering tools to improve productivity while maintaining security, quality, and governance. Strong communication skills with the ability to translate technical concepts for business audiences. Preferred Skills Hands-on Talend experience (Talend Cloud and/or Remote Engine) in an enterprise environment. Experience working in hybrid cloud and on-prem data ecosystems. Familiarity with architecture and change governance processes. Experience implementing metadata/lineage practices or data catalog tools. Experience supporting enterprise BI platforms such as Power BI. Exposure to data preparation patterns that support ML/predictive analytics initiatives. Equal Opportunity Statement We are committed to diversity and inclusivity in our hiring practices. #J-18808-Ljbffr