Assistant Vice President of Data, Analytics & AI Engineering

Anaplan; Manchester; On-site; GBP 170,000 - 210,000; Full time

Job summary

Anaplan in Manchester seeks an AVP, Data, Analytics & AI Engineering to set the strategic vision, architecture, and scaling of our enterprise data platforms and AI solutions.

You will lead a cross-functional team to turn data assets into high-performance, secure intelligence frameworks that drive automation and strategic decision-making across the company, bridging cloud data stack engineering and AI paradigms.

Qualifications

• 12+ years in data and software engineering environments with experience in senior management or executive roles
• Experience moving to modern data architecture environments with data warehouse/lakehouse (AWS Redshift, Databricks), streaming/batch, APIs, data products, ELT, CDC, structured + unstructured data
• Ability to translate complex agentic systems and distributed database details into clear business narratives for non-technical and executive boards
• Proven experience with AI/ML engineering and deploying ML models into production, feature engineering, model serving, monitoring, MLOps
• Strong familiarity with cloud data ecosystems (Snowflake, Databricks, Vertex AI, AWS Sagemaker)
• Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, AI/ML, Math or similar highly quantitative field

Responsibilities

• Lead the strategic vision, architecture, development, and scaling of enterprise data platforms and AI solutions
• Turn enterprise data assets into high-performance, compliant, and secure intelligence frameworks that drive automation and strategic decision-making across the company
• Bridge the gap between modern cloud data stack engineering and emerging AI paradigms (LLMOps and Agentic workflows)
• Ensure Anaplan engineering standards drive direct business value while maintaining top-tier data security, governance and responsible AI practices
• Scale the team by building, mentoring, motivating and leading a high-performance group across Data Engineering, Analytics Engineering and AI Engineering
• Partner with CFO, CRO, CHRO, CIO and Product leaders to deliver AI and data solutions aligned to enterprise goals
• Drive Advanced Analytics/Decision Intelligence with predictive analytics, propensity, forecasting, experimentation, causal thinking, optimization
• Architect and govern Enterprise Agentic AI Strategy with multi-agent frameworks and LLM-powered tools at scale
• Deploy enterprise-grade multi-agent AI systems integrating with ERP, CRM and ITSM to automate high-value decisions end-to-end
• Drive Anaplan beyond pipelines feeding dashboards into a Modern Data Architecture environment

Skills

Executive leadership

Strategic thinking

Cross-functional leadership

Discretion

Mentorship

Education

Bachelor’s or Master’s in Computer Science

Data Engineering

AI/ML

Mathematics

Tools

Snowflake

Databricks

Vertex AI

AWS Sagemaker

AWS Redshift

ERP integration

CRM integration

ITSM

Job description

• The AVP, Data, Analytics & AI Engineering is the executive leader responsible for the strategic vision, architecture, development, and scaling of our enterprise data platforms and AI solutions
• This individual will lead a team tasked with turning enterprise data assets into high-performance, compliant, and secure intelligence frameworks that drive automation and strategic decision-making across the entire company
• The role bridges the gap between modern cloud data stack engineering and emerging AI paradigms (like LLMOps and Agentic workflows)
• This individual will ensure that Anaplan engineering standards drive direct, measurable business value while maintaining top-tier data security, governance and responsible AI practices
• This is a critical role that requires an executive and organizational leader with a high degree of enterprise transformation leadership, discretion, and the ability to lead multiple priorities in a fast-paced, sales-driven, SaaS environment
• This individual will work directly with the CFO, CRO, CHRO, CIO and Product leaders while building a high-performance engineering team
• Create Advanced Analytics/Decision Intelligence driving predictive analytics, propensity, forecasting, experimentation, causal thinking, optimization
• GenAI/Agent Engineering that is essential to the enterprise agent strategy - MCP, RAG, embeddings/vector search, tool calling, agent architecture, evaluation, grounding, orchestration. Architect and govern the Enterprise Agentic AI Strategy, deploying multi-agent frameworks and LLM-powered tools at scale
• Architect and deploy enterprise-grade multi-agent AI systems for complex, multi-step workflows, integrating with ERP, CRM, and ITSM to automate high-value decisions end-to-end
• Design internal AI tools and agents (e.g., AI Finance Bot, domain-specific LLM applications) and lead the roadmap for next-generation agentic platforms as emerging capabilities mature
• Drive Data Governance & AI Readiness - quality, lineage, observability, catalog, access controls, PII, retention, AI data governance
• Provide business context to a Semantic & Context Architecture with metrics layer, business ontology, entity relationships, metadata, knowledge graphs/semantic models
• Scale the team - build, mentor, motivate and lead high-performance team across Data Engineering, Analytics Engineering and AI Engineering
• Partner collaboratively with business units, product teams, security, legal and compliance teams to deliver AI and data solutions that align to enterprise goals
• Drive Anaplan beyond pipelines feeding dashboards into a Modern Data Architecture environment

Leadership experience of 12+ years in data and software engineering environments, with at least 5 years in a senior manager or executive role leading cross-functional teams

Experience moving to modern data architecture environment, with Deep Datawarehouse/lakehouse (AWS Redshift, Databricks), streaming/batch, APIs, data products, ELT, CDC, structured + unstructured data experience

Exceptional ability to translate complex agentic systems, context-engineering strategies, and distributed database details into clear, business-relevant narratives for non-technical and executive boards

Proven experience with AI/ML engineering and success deploying ML models into production, feature engineering, model serving, monitoring, MLOps

Strong familiarity with core technical stack - cloud data ecosystems (eg. Snowflake, Databricks, Vertex AI, AWS Sagemaker)

Education: Bachelor’s or Master’s degree, Computer Science, Data Engineering, AI/ML, Math or a highly quantitative field of study