K3 Advisory Group Hiring! Full Time Data Lead in Federal Territory - Ricebowl

About K3 Advisory Group

K3 Advisory Group is a UK professional services group with multiple trading subsidiaries and more than 1,200 staff. The Group spans corporate finance, tax, restructuring and insolvency, legal, financial planning, and technology-enabled advisory services.

All technology delivery must balance the pace required to exploit AI, data and automation with strict expectations around governance, regulatory obligations, security-by-design, audit trails, and client confidentiality.

Group Technology builds shared platforms, data foundations and AI-assisted products that scale across these businesses while accommodating local variation. Engineers in this team work close to commercial outcomes, with direct visibility of advisors, partners and clients.

Our data & AI platform

Our estate is predominantly Azure. We build on Azure-native data services — including Microsoft Fabric, Synapse, Data Factory and, on some products, Cosmos DB — with Databricks part of our wider reference stack, and we make heavy use of PostgreSQL. We keep the platform aligned with the latest Azure capabilities and the Group's technology direction, so we value engineers who are comfortable evolving an estate, not just maintaining one.

Our data teams support both internal platforms and the SaaS products the Group sells, with a growing focus on integrating third-party and external data across multiple products. Governed integration is central to how we work: well-designed APIs, semantic layers and modern integration standards such as the Model Context Protocol (MCP) connect data with applications and AI-assisted products under strict access control, logging and audit. Reconciliation, pipeline reliability and the safe, permissioned exchange of data with third-party systems are core to how the platform earns trust.

Role Purpose

Lead the data engineering function across K3 Advisory Group. Own the data platform strategy and roadmap, set and enforce the engineering and governance standards the team works to, and line-manage the Kuala Lumpur-based team of junior, mid-level and senior data engineers who build the pipelines, models and semantic layers that power analytics and AI across all K3 brands. The function's remit spans internal data platforms and the Group's client-facing SaaS products, with a strong emphasis on third-party and external data integration.

This is a player-coach leadership role: it combines hands-on architectural authority with people leadership and delivery accountability. The Data Lead is the person the business trusts to answer three questions at any time: is the data right, is it safe, and is the platform ready for the next acquisition. Tenancy, lineage, quality and auditability are not aspirations — they are the baseline the Lead is accountable for across every subsidiary, including FCA-regulated entities.

Key Responsibilities

Strategy & Architecture

• Own the Group data platform strategy, target architecture and roadmap on our Azure estate — including the balance between PostgreSQL and lakehouse tooling (Fabric/Databricks/Synapse), semantic layers and serving patterns — and keep it aligned to commercial priorities across the Group's brands.
• Own the platform evolution roadmap: keep the estate aligned with the latest Azure capabilities and the Group's technology direction, introducing, evolving or retiring components with clear sequencing and consumer impact analysis, without disrupting advisor-facing outputs.
• Make and document the material architecture decisions: platform and storage selection, modelling conventions, lineage approach, metric definition processes, API and data contract standards.
• Own the Group's strategy for governed data integration with applications, AI-assisted products and third parties: API standards, MCP server architecture and guardrails, and the approval path for new integrations.
• Own the patterns for third-party and external data: sourcing, quality assessment, licensing awareness, ingestion, enrichment and permissioned exposure across internal platforms and the Group's SaaS products.
• Balance local flexibility with group-wide standards — define what is mandatory everywhere (tenancy, security, auditability, quality gates) and where subsidiaries can vary.
• Own the repeatable acquisition data-onboarding playbook so new businesses land on the platform through a documented pattern, not bespoke effort each time.
• Manage platform cost, capacity and vendor relationships, with a clear view of unit economics per brand and per workload.
  
  

Team Leadership & Standards

• Line-manage the data engineering team — junior, mid-level and senior data engineers — based in Kuala Lumpur, including hiring, objectives, development plans, progression decisions and performance.
• Run the team's development framework: mentoring pairings, progression criteria from Junior through Senior, and succession depth at every level.
• Set and enforce the engineering quality bar: CI/CD, code review, testing, documentation, naming standards, runbooks and data governance practices.
• Run the team's delivery rhythm — prioritisation, capacity planning, SLAs/SLOs for critical datasets, incident response and post-incident learning.
• Act as final escalation point for data incidents affecting advisor-, partner- or client-facing outputs.
  
  

Governance, Risk & Regulatory

• Be accountable for tenancy, permission-aware access, lineage and auditability across all data domains, including FCA-regulated entities (e.g. Pareto, Luna).
• Partner with compliance, risk and information security to ensure data handling meets UK GDPR, FCA record-keeping expectations and Group security-by-design standards.
• Own the data quality framework: definitions, thresholds, reconciliation controls and reporting to Group Technology leadership.
• Ensure applications, AI-assisted products and third-party systems integrate with platform data only through approved, governed interfaces — curated semantic layers, metric stores, APIs and MCP tools — never raw access; own the review and audit of those interfaces.
• Represent data governance and platform risk at senior stakeholder and, where required, board-level forums, in clear, decision-ready language.
  
  

Delivery & Stakeholder Management

• Translate business needs from partners, subsidiary MDs and product owners into data architecture and delivery plans with clear trade-offs.
• Own the semantic contracts that let product, dashboard and AI teams build without bespoke rework — 'build it once, use it many times' is a KPI, not a slogan.
• Sequence acquisition integrations and platform evolution work alongside product commitments, making dependencies and risks visible early.
• Contribute hands-on where it matters most: reference implementations, gnarly design problems and unblocking the team.
  
  

Required Experience & Skills

• 8+ years commercial data engineering experience, including 2+ years leading or managing data engineering teams across multiple seniority levels.
• Track record of owning a cloud data platform end to end — architecture, delivery, cost, reliability and governance — ideally on Azure.
• Deep, hands-on expertise in SQL and Python, including production PostgreSQL at scale, plus lakehouse tooling (Databricks/Synapse) and modern orchestration.
• Proven experience designing semantic layers, metric definitions, data contracts and governed multi-tenant access patterns.
• Strong experience with API strategy: designing, versioning and governing data interfaces consumed by third-party systems and internal products.
• Credible working knowledge of data patterns for AI/LLM products — RAG, retrieval datasets, evaluation data — and of MCP or equivalent governed tool-based integration standards, sufficient to set standards and review designs.
• Sets the team's standard for AI-assisted engineering: productivity gains from AI tooling are welcomed, but every change is understood, reviewed, tested and explainable.
• Demonstrable people leadership: hiring, developing and retaining engineers from junior to senior, ideally in teams working closely with overseas (e.g. UK) stakeholders.
• Experience operating in a regulated or high-governance environment, with practical fluency in data protection, auditability and access control.
• Experience leading platform migrations and integrating acquired businesses or consolidating multiple source systems onto a shared platform.
• Strong stakeholder skills at senior/executive level — able to present trade-offs, risks and recommendations in decision-ready form.
  
  

Desirable Experience

• Professional services, financial services or legal data domains (client/matter/case, finance, risk, pipeline).
• Hands-on ML experience or close partnership with ML/AI engineering teams (feature pipelines, model data, evaluation).
• Azure-native tooling depth: Microsoft Fabric, Databricks, Synapse, Data Factory, Purview, and Azure security/identity patterns.
• Data mesh-style domain ownership or data product operating models, applied pragmatically.
• Experience presenting to boards, audit committees or regulators on data or technology risk.
  
  

Success Measures

• Platform reliability: Critical datasets meet agreed SLAs/SLOs; incidents are rare, well-handled and followed by durable fixes.
• Platform evolution: Platform changes land to plan without disruption to advisor-, partner- or client-facing outputs, and the estate demonstrably keeps pace with Azure and Group direction.
• Reuse & speed: New products, dashboards and AI use cases build on existing contracts, APIs and MCP tools; time-to-onboard a new data source or acquired business falls release over release.
• Quality & trust: Data quality is measured, reported and improving; business leaders trust platform numbers over local spreadsheets.
• Governance: Tenancy, permissions, lineage and auditability hold across all subsidiaries — including AI and third-party access paths — evidenced through audit and review.
• Team health: The team retains and develops its people from junior to senior, has succession depth, and delivers predictably against its roadmap.
  
  

Working Environment

• Reporting line: Group Technology leadership, with matrix relationships into subsidiary leadership, compliance and risk.
• Team: Line management of junior, mid-level and senior data engineers based in Kuala Lumpur, working alongside AI/ML engineering, full-stack engineering, product and design, and collaborating daily with UK-based stakeholders.
• Stakeholders: Group Technology leadership, subsidiary MDs and partners, compliance and risk functions, and client-facing advisors.
• Delivery model: Iterative, product-led delivery with short feedback loops, paired with the governance discipline appropriate to a regulated professional services environment.
• Tooling baseline: Azure-first cloud platform, PostgreSQL and Azure-native data services, Git-based source control, CI/CD pipelines, infrastructure-as-code, observability tooling and a documented engineering handbook.
• Ways of working: Code review, pairing, design reviews, threat modelling for sensitive features, and lightweight architecture decision records (ADRs).
  
  

Governance, Security & Compliance Expectations

• Every engineer in Group Technology is expected to treat the following as non-negotiable foundations, not optional extras:
• Confidentiality: Client, matter, and case data is highly sensitive. Need-to-know access is the default; broad access is the exception and must be justified.
• Security by design: Threat modelling, secure defaults, secrets management, dependency scanning and least-privilege access are built into features from day one.
• Auditability: User actions, data access and administrative changes are logged in a tamper-evident, queryable form suitable for internal audit and regulatory review.
• Responsible AI: Where AI is used, model behaviour, prompts, tools and data access are versioned, evaluated and monitored. Applications, AI-assisted products and third-party systems integrate with data only through approved, governed interfaces (APIs, semantic layers, MCP servers) — never raw access. Human oversight is preserved for material decisions.
• Regulatory awareness: For features touching FCA-regulated entities (e.g. Pareto, Luna), additional controls apply around record-keeping, client communications and data handling. Engineers are expected to flag uncertainty early.
• Data protection: UK GDPR, Malaysian PDPA (where applicable) and Group data protection standards apply across all subsidiaries; data minimisation, lawful basis and retention controls are part of normal design.
  
  

Development & Progression

• Natural progression toward Head of Data / Head of Engineering or principal architecture routes as the Group's data and AI footprint grows.
• Direct exposure to M&A activity: due diligence support, integration planning and post-acquisition platform consolidation.
• Supported learning budget, relevant certifications and conference participation.
• Direct line of sight to commercial outcomes and senior leadership across the Group.
  
  

Person Specification

• Leader: Grows engineers at every level, sets standards others want to meet, and takes accountability without taking credit.
• Decision-maker: Trusted with material platform decisions; makes trade-offs explicit and commits.
• Governance-aware: Sees tenancy, lineage and auditability as enablers of speed, not blockers of it.
• Commercially grounded: Prioritises by business value and ROI, and can defend the roadmap in those terms.
• Pragmatic: Knows when a simpler model serves the business better than a more sophisticated one, and when to invest in the harder path.
• Communicator: Translates fluently between engineers, advisors, compliance and executives — in writing and in the room.
• Creative problem-solver: Thinks outside the box on hard problems, then lands on pragmatic, supportable solutions the team can own.
  
  

Location

• Location: Kuala Lumpur, Malaysia (hybrid working model).
  
  

With over 1,200 employees across the Group, 25 offices in the UK, and international bases in Malaysia and Cyprus