Key Responsibilities:

Develop and implement portfolio Data modelling, assurance and utilisation strategies and frameworks that align with enterprise approved governance, data and technology strategy and the Data COE. Lead the implementation of these strategies within the portfolio.

Serve as though leader and guide in the data domain by sharing knowledge identifying problems, patterns, trends, and support the development of relevant BI and MI solutions. 

Design and implement scalable and robust processes for ingesting and transforming complex datasets.

Contribute to the development of architectural frameworks, apply architecture principles, and drive the development of data architecture models within the organization.

Design and develop data models using dimensional modelling and data vault techniques and ensure stated business requirements are met by these models. 

Architect, train, validate and test advanced analytics / machine learning models, using enterprise-grade software engineering practices.

Design, develops and maintain automated scalable data pipelines that improve estate performance, stability and auditability. These include data pipelines for ETL processing. Monitor and troubleshoot data pipeline issues.

Stakeholder Communication

Excellent communication and presentation skills for effectively conveying data status, data-driven insights, and recommendations to stakeholders at all levels.

Ethical and Compliance Awareness

Understanding of ethical considerations in data engineering, including data privacy, security, and confidentiality.

Continuous Learning and Adaptability

Commitment to staying updated with emerging data engineering trends, technologies, and industry developments.

Minimum Qualifications/Experience 

Bachelor's or master’s degree in computer science, Information Technology, or a related field.

10+ years of experience in data engineering with a focus on leadership and project management.

Data warehouse technical experience – definition /implementation/ integration.

Strong programming skills in Python and DBA skills (SQL/PSQL/DynamoDB or other).

Experience with data pipeline and ETL tools and reporting/analytics tools including, but not limited to, any of the following combinations (1) SSIS and SSRS, (2) ETL Frameworks, (3) Data conformance, (4) Caching, (5) Spark (6) AWS data builds.

Experience with data modelling, data governance, and data quality.

Strong problem-solving skills and ability to work in a fast-paced environment.

Strong communication skills and ability to work in a team.

Expertise in Machine Learning (ML) and deep learning frameworks.

Explaining the thinking behind simple ML algorithms.

Proficiency in all aspects of model architecture, data pipeline interaction, and metrics interpretation.

Additional 

Experience with Big Data technologies such as Hadoop and Spark.

Experience with containerization technologies such as Docker and Kubernetes.

Competencies Required ​

Multi-functional team Collaboration (Relating)

Customer First

Execution

Innovation (Perspective)

Leading with Influence

Learning

Strategic thinking

Personal Mastery