Role:                Director of Data Engineering                                   
Reports to:      SVP of Engineering
Location:         Edinburgh

Role Purpose

The Director of Data Engineering will lead the team of Data Engineers in managing data through its lifecycle. At Wood Mackenzie, data, a product in its own right, is also the foundation on which our high-value analytics and research is based. Our competitive advantage is the quality of our data – it’s completeness, coverage, and accuracy. Our high data quality is critical to our success and this newly developed role is part of a transformation we are undergoing to create a new data organization that is better equipped to produce the high-quality data we need to provide more differentiated, more compelling services for our clients in the energy and natural resources markets.

As a member of the Central Data Organisation, this is a key role that will transform how we manage and process our data. We are looking to become more scalable, highly automated, and capable of producing higher quality data that further differentiates us from our competitors. We are looking for a leader who can run a global operational team with employees located in Edinburgh, London and Houston. The data operations team will use high levels of automation and careful process control to take raw, often unstructured and dirty data and turn it into structured, quality-controlled, analytics-ready data. This role requires understanding of technology and processes for managing data and the ability to interface with senior and executive leaders within the data organization and across the company.

As a senior leader in our Central Data organisation this is a pivotal role to build out and transform our data operations within Wood Mackenzie.

Role Responsibilities

Strategy & Vision

• Lay out the path and lead the hiring and development of a highly efficient operational team of data engineers responsible for moving data through our data pipeline
• Collaborate with senior stakeholders across the business to understand the evolving operational data needs of the organisation and our clients.
• Collaborate with members of the Research Team (data analysts and research analysts) to deliver new data needs.
• Work closely with the Heads of Data in Research and the CDO to ensure strong communication and alignment with Research Leadership Team (RLT) priorities.
• Once a new data source has been identified estimate work required to onboard and validate new data sets.

Leading Teams

• Hire data engineers at locations as determined to be optimal by our leadership team.  This includes establishing a team in Krakow, augmented by employees in locations including Edinburgh, London, and Houston.
• The team will be responsible for sourcing raw data, extracting structured data from unstructured documents, aligning data to a data model and validating it.
• Work with CDO to agree key workforce planning & resource priorities for the Operations team and track progress against these – specifically with respect to any special projects, timing, prioritisation and budget.
• Working with teams that develop proof-of-concept capabilities and finding ways to integrate them into a production data pipeline.
•  Leading teams to produce reports and dashboards that show the status of data in our pipeline
  • • Show key metrics about types of and volume of data processed
    • Highlight areas of concern that require follow-up
    • Show trends in our processing capability over time
    • Help in finding gaps in coverage, completeness, quality
    • Segmented to identify data strengths and weaknesses
• Develop the Operations team to ensure we have a breadth of developing expertise and provide input on the resourcing and capability gaps we are likely to face in the future to cover our evolving data needs.
• Foster an agile, high-performance, collaborative culture which is creative, open, supportive and dynamic with high levels of continuous improvement mind-set.

Operational Excellence & Quality

• Ensure the team ways of working are aligned and compliant with the organisation’s defined methodologies and approaches.
• Work with stakeholders across Wood Mackenzie to ensure effective timely delivery on key deliverables and business cycle processes.
• Able to work with research teams in understanding how clients view data quality and using that understanding to implement validation processes to enforce that quality
• Ensure strong team emphasis on automation and scalability
• Define and monitor metrics that measure scalability of Operations team Proof of Concepts.
  • • Track these metrics over time to improve performance
    • Define technical requirements that increase the level of automation in the Data Operations division.
• Strong emphasis and understanding of data quality
  • • Ensure that the right data is available to research analysts and end-users to produce high quality, timely data-driven analysis, insight and content.
    • Define metrics to measure data coverage, data completeness, and data accuracy
    • Lead the team to develop dashboards and reports for measuring these metrics
• Ensure that Lean principles and philosophy are at the forefront of WM thinking and ways of working.
• Monitor, produce and distribute as required all metrics used by the team to drive performance
• Learn and understand our data pipeline and the tools used to manage data through that pipeline

Knowledge & Experience Required

• Ideally you will already be a senior leader in a subscription data analytics business, but we will consider someone ready to move into a senior data operational leadership focused role.
• You will have a strong understanding of data management fundamentals, including concepts such as data dictionaries, data models, validation, and reporting.
• Able to manage a team in using data tools to create validation rules (both simple and complex)
• Able to lead teams in building dynamic dashboards, reports, and presentations on data quality and data status
• Understand different ways to do validation, including:  business rule, point in time, period-over-period, trend, inclusion of other data elements
• Proven track record of involvement in setting up and efficiently managing operational data teams in finance or natural resources industry.
• Experience in Oil, Gas and Power industry would be an advantage but not essential
• Ability to manage complex relationships in a matrix environment and strong stakeholder management skills.
• Experience managing data through its lifecycle (especially sourcing, validating, and deploying)
• Experience with Python or other general-purpose programming language
• Experience with BI tools such as PowerBI, Tableau, Spotfire
• Experience with ETL and data manipulation tools helpful, such as Pentaho or Alteryx
• Experience with different types of databases, including structured and unstructured
• Experience querying and manipulating data stored in databases to build reports or dashboards
• Experience managing automated systems
• Experience in troubleshooting data issues (related to any part of the data lifecycle of sourcing, validating, data model alignment, validation, publication) 
• Results orientated, proactive, takes initiative.
• Strong organizational and project management skills
• Ability to work across regions and cultures
• Willingness to travel to locations where operational teams may be located (Edinburgh, London, Houston, Krakow)

Leadership Capabilities

People Advocate

• Inspires & motivates other to higher performance
• Identifies and invests in talent

Owner Operator

• Accountable for driving results
• Makes timely decisions
• Values customer insight

Innovator

• Initiates and Leads Change
• Fosters innovation

Culture Creator

• Lives the Verisk Way (https://www.verisk.com/veriskway/)
• Communicates effectively
• Collaborates effectively across business and geographies

 Core Competencies

• Planning, implementation and control
• Issue identification, problem solving and analysis
• Efficiency focused
• Determined and resilient
• Assertive and Influential
• Client focused

• #LI-DNI

All your information will be kept confidential according to EEO guidelines.