Responsibilities:

- Lead, hire and mentor a high-performing team; set priorities, delivery cadence and engineering standards to scale the capability.

- Own the roadmap and delivery of enterprise data platforms and analytics products, including data lakes/warehouses, ETL/ELT, streaming and data APIs.

- Provide hands-on technical leadership: design and review architecture, implement or optimize key components, and resolve production incidents as required.

- Drive adoption of Databricks best practice (Spark optimization, Delta Lake, Unity Catalog) and core cloud platform patterns.

- Productionise AI/ML: guide feature engineering, model deployment, monitoring, versioning, explainability and MLOps workflows.

- Establish and enforce CI/CD, automated testing, observability and incident response for data pipelines, models and analytics services.

- Define and enforce data governance, security, privacy and compliance standards in partnership with legal and security teams.

- Partner with business leaders, product managers and consulting teams to translate business problems into scalable data and AI solutions; manage stakeholder expectations and prioritisation.

- Manage vendor relationships and platform budgets; evaluate and procure third-party tools where appropriate.

- Foster a culture of data literacy, experimentation, inclusivity and continuous improvement.

Must have skills and qualifications:

- Degree in Computer Science, Engineering, Data Science, Statistics or equivalent practical experience.

- 15+ years designing and delivering data platforms or large-scale data systems; 5+ years managing engineering teams and senior technical leaders.

- Proven experience delivering end-to-end cloud data platform transformations at enterprise scale.

- Hands-on Databricks experience (Spark optimisation, Delta Lake, workspace/job orchestration, Unity Catalog) at scale.

- Strong practical experience building and operating data solutions on AWS (e.g., S3, Glue, Redshift/Athena, Lambda, EKS/ECS; infrastructure as code such as CloudFormation or Terraform).

- Solid understanding of AI/ML production patterns, including model development, deployment, monitoring, drift detection and MLOps.

- Strong software and data engineering skills: Python and SQL required; Scala/Java advantageous. Experience with Spark, data modelling, ETL/ELT and streaming fundamentals.

- Experience implementing CI/CD, container orchestration and observability for data systems.

- Knowledge of data governance, metadata/catalogue tools, lineage and data quality frameworks (i.e. Great Expectations or equivalent).

- Strong grasp of security, data privacy and regulatory requirements (e.g., GDPR, data residency).

- Professional certification such as Databricks or AWS (Solutions Architect / Specialty).

What makes you stand out:

- Consulting or client-facing delivery experience.

- Experience with streaming platforms (Kafka, Pub/Sub, Kinesis) and real-time architectures.

- Familiarity with generative AI, LLMs and conversational AI production patterns.

- Exposure to other cloud providers (Azure/GCP) or hybrid cloud architectures.