Technical Lead in Manchester

We are seeking a Technical Lead to join our Data, Analytics & AI team within a professional services environment. The successful candidate will provide technical leadership across the firm’s enterprise data landscape, ensuring that core integrations, pipelines, and platform services are reliable, efficient, visible, and consistently delivered to agreed data engineering standards.

Operating across the firm’s enterprise data platforms, the role sits at the intersection of data architecture, platform engineering, service management, and delivery governance. The Technical Lead will act as the bridge between published data architecture and day‑to‑day standards, reusable engineering patterns, and clearly defined work packages for the data engineering team.

The role requires close collaboration with data architects, security, service management, internal stakeholders and delivery teams to ensure our enterprise data team provides stable, supportable, scalable solutions that align to business priorities.

You Will Be Expected To

• Provide technical leadership for the firm’s enterprise data platforms, integrations, pipelines and related platform services.
• Review technical designs and engineering outputs to ensure they align with agreed architecture, platform principles and operational standards.
• Own the data engineering standards covering design, development, testing, deployment, monitoring, and documentation.
• Define reusable implementation patterns and technical work packages.
• Remove engineering duplication, technical debt, and inconsistent approaches.
• Provide hands‑on technical input or delivery where critical, complex or high‑risk.
• Own the technical design of enterprise data integrations across core systems, operational platforms and downstream data services.
• Define integration patterns for batch, scheduled, event‑driven, API‑based and file‑based data movement where appropriate.
• Ensure pipelines are optimised for reliability, performance, security, observability and operational recovery.
• Support the development of reusable pipeline patterns that can be applied consistently across enterprise data workloads.
• Drive improvements in pipeline efficiency, maintainability and transparency.

CI/CD, Release, Governance & Operational Control

• Own CI/CD standards for enterprise data engineering assets, including pipelines, scripts, notebooks, configuration and deployment artefacts.
• Ensure data engineering changes are assessed for risk, impact, security, access control, auditability and operational readiness before release.
• Promote appropriate monitoring, logging, lineage, documentation and support hand‑over for enterprise data pipelines and platform services.
• Work with architecture, security, and service teams to ensure data engineering standards reflect firmwide governance, compliance and quality requirements.

Change, Incident & Problem Management

• Provide technical leadership for change, incident and problem management relating to enterprise data platforms, integrations and pipelines.
• Support incident resolution and root cause analysis for material or recurring data engineering issues, ensuring corrective actions are captured and implemented.
• Translate operational issues into preventative improvements, including stronger standards, reusable patterns, monitoring enhancements or platform changes.

Platform Evolution & Tooling

• Identify opportunities to improve enterprise data platform reliability, performance, observability, automation, cost efficiency, and developer experience.
• Assess new or enhanced tools and platform capabilities in partnership with architects and other technical leaders.
• Ensure platform and tooling changes are introduced in a controlled, supportable and well‑documented way.

Stakeholder Collaboration & Enablement

• Work closely with data architects, technical teams, business partners, and internal stakeholders to align technical delivery with business priorities.
• Provide regular, consistent updates on project progress and own the timely and clear communication of blockers, delays or operational issues.
• Provide technical guidance on enterprise data platform capabilities, constraints, risks and recommended approaches.
• Support knowledge sharing across delivery teams through standards, reusable patterns, work package guidance, runbooks and technical documentation.

Essential Skills and Experience

• Senior experience in data engineering, enterprise data platforms or data integration within an enterprise environment.
• Strong experience of designing and supporting data integration patterns, pipeline design, orchestration, transformation and data platform services.
• Proven ability to translate data architecture into implementable technical standards, patterns and delivery work packages.
• Strong understanding of CI/CD, source control, incident and release management and deployment practices for data engineering assets.
• Ability to define clear technical requirements, acceptance criteria, testing expectations and support handover requirements.
• Experience working with outsourced, offshore or ITSM‑managed delivery teams.
• Strong understanding of data security, access control, auditability, monitoring and operational resilience.
• Strong communication skills, with the ability to explain technical decisions, risks and trade‑offs to technical and non‑technical stakeholders.
• Hands‑on technical credibility in relevant data engineering technologies such as SQL, Python, orchestration tools, cloud data platforms, APIs, file‑based integrations or data warehouse/lakehouse platforms.

Desirable Skills and Experience

• Experience operating in a professional services, consulting, or complex enterprise.
• Experience of working in a controlled SDLC environment, with experience of scrum and waterfall project delivery.
• Familiarity with Microsoft Azure data platforms, Microsoft Fabric, Azure Databricks, Azure Data Factory, Synapse, Event Hubs or equivalent technologies.
• Experience with medallion architecture, data lakehouse design, data warehouse modernisation or enterprise data product patterns.
• Experience defining and improving DataOps practices across engineering teams.
• Experience with infrastructure‑as‑code, automated deployment or environment management.
• Knowledge of data governance, lineage, metadata management and data quality frameworks.
• Experience contributing to technical roadmaps, platform evolution or engineering capability improvement.
• Relevant certifications in Azure, data engineering, DevOps, architecture, ITIL.