Data Engineering Lead- Conformed Data Warehouse - BI Developer

THE SUCCESSFUL CANDIDATE MUST HAVE A
BSc Computer/Data Science OR
BCom Informatics or
BEng Computer OR
BSc Mathematics / Statistics DEGREE FROM ONE OF THE 6 LEADING UNIVERSITIES.

7+ years in Business Intelligence/Data Analytics/Data Science/Modelling/Statistics
Programming and statistical computer languages (R, Python , SQL, etc.) Working knowledge of Business Intelligence principles & best practices

Accountable for the delivery of Business Intelligence solutions and Professional Services, support data warehouse and management of data quality, that will enable Imperial to make faster and more informed decisions based on insights generated from this capability.

It is expected that the Data Engineering Lead perform both individual technical work as well as facilitate, guide and provide oversight of team delivery, with a 40%/60% split between individual technical delivery vs team delivery

Support:

• Inform strategic and faster decision making through the availability of critical data sets, insights and modelling for D&IT and Imperial
• The team to achieve joint targets and deliverables
• Reliable, On-Time, Efficient & Quality Delivery as per the chosen process & tooling

• Mitigation plans are in place for work items that are not going to be delivered according to the estimated timeline
• Insights are always rigorously tested and approved before releasing to D&IT and Imperial

For both individual and team deliverables, ensure:

• Reliable, On-Time, Efficient & Quality Delivery as per the chosen process
• Mitigation plans are in place for projects/programmes that are not delivering according to the estimated timeline
• Insights are always rigorously tested and approved before releasing to D&IT and Imperial

People & Culture:

• Provide hands on leadership (people & technical) to the team
• Provide oversight & support of seconded resources embedded into businesses across the group
• Ensure Team wellbeing
• Build and maintain a healthy working relationship within the team
• Shape and enforce team culture & norms
• Drive continuous knowledge sharing initiatives
• Drive continuous improvement initiatives

Process and delivery:
Be the Team Lead for a team of Business Intelligence Developers:

• Help manage team performance as part of The team must reliably deliver on sprint backlog
• Help ensure team efficiency through accurate work estimates and completion against estimates as per sprint plan

• Help manage technical work allocation per Facilitate Sprint planning 2: How to solve problems
• Help ensure quality of team
• Help ensure process & tooling adoption and utilization
• Help ensure the technical competence of the Team

Technology and standards (Where applicable):

• Define, document and communicate design patterns
• Ensure the definition, documentation and communication of solution designs before implementation
• Ensure as-built documentation is done according to standards, and is continuously updated to ensure accuracy
• Ensure that solutions are measured against the Definition of Done
• Ensure the WIKI is periodically reviewed and updated for content correctness and structure
• Enforce source code management practices
• Enforce quality assurance practices

Adhere to:

• all corporate governance, processes, procedures and statuary, legal and other requirements

Behavioural Competencies

Entrepreneurial and Commercial Thinking Persuading and Influencing
Deciding and Initiating Action Leading and Supervising
Adapting and Responding to Change Coping with Pressures and Setbacks Presenting and Communicating Information Relating and Networking
Adhering to Principles and Values

Technical Competencies
Excellent analytical and problem-solving skills Resource and people management
Strong communication skills Strong relationship building skills
Excellent planning, time management and organisational skills

High level knowledge or awareness of statistical modelling and data mining techniques i.e. Regression, Random Forest , Boosting, Trees, text mining, social network analysis etc.
Querying databases using statistical computer languages i.e. R, Python, SQL, etc. Knowledge of machine learning algorithms