Head of Data Engineering

The Head of Data Engineering is primarily responsible for leading the data engineering team, overseeing the development, deployment, and maintenance of scalable data infrastructures and pipelines. The Head of Data Engineering will be responsible for the execution of the overall Data strategy for Digital transformation. They will work closely with the business, digital, and IT teams to develop the data architecture to enable the digital and AI/ML use cases. The Head of Data Engineering also drives the team's data engineering practices and acts as a coach for data engineers on the use-cases and create a best-in-class engineering capability.

KEY ACCOUNTABILITIES:

1. Execution and implementation of the data management strategy
2. Design overall data architecture with best-in-class concepts and technologies in mind
3. Guide project teams on data strategy related to coding practices, data engineering, and analytical modelling
4. Coach team in coding to develop robust data pipelines and architecture solutions
5. Advocate and educate on the value of data driven decision making focusing on the "how and why" of solving problems
6. Work with Product Owner to align the roadmap with strengths and opportunities within the data architecture
7. Evolve data engineering solutions continuously and challenge the status quo
8. Execute organisation vision of Industry 4.0 by developing and implementing short-term and long-term data strategies.
9. Develop the long-term data roadmap in collaboration with IT teams around platform selection and usage
10. Provide the team with a roadmap for implementing data engineering best practices and build data engineering capability by providing technical leadership and career development for the organisation
11. Lead, mentor, collaborate and coach a growing team of Data Engineers fostering a culture of innovation and agility.
12. Devise talent strategy for the team - identify resource gaps or inefficiencies, build hiring plan, recommend promotions and manage performance for consistency with the needs and vision of the department.
13. Lead multi-functional delivery teams to deliver robust data services for their department, other departments and customers.
14. Work with other senior team members to identify, plan, develop and deliver data services
15. Perform key leadership and mentorship role in the areas of advanced data techniques, including data modelling, data ingestion, data integration, data visualization, data governance, statistical methods, and implementation involving cloud-based, traditional and hybrid architectures.
16. Review, select and integrate next-generation technologies into the architectural strategy as needed.
17. Work closely with the Data Science / Machine Learning and Analytics teams to provide data required for developing, operationalizing, monitoring and evaluating predictive models, dashboards and reports for customer facing applications and internal use.
18. Implement and monitor quality control processes to ensure accuracy of data, dashboards and reports.
19. Lead the development and implementation of the data governance process. Governance must address data standardization and ways to address exponential data growth, and include establishing Data Owners and Stewards.
20. Be an SME in technical and functional aspects of our insights platform and its integration requirements
21. Proactively identify internal and external dependencies, issues, scope changes, and progress against project plan
22. Provide technical support to assist customers during and post implementation
23. Build and lead high-performing, agile teams focused on Data Engineering.
24. Initiate and foster business partnerships with customers, vendors, IT executives, and senior business executives

AUTHORITY/ DECISION MAKING:

1. Overall implementation of data infrastructure for scalability and performance.
2. Oversee hiring, training, and resource allocation for the team.
3. Prioritise data engineering projects based on business needs.
4. Manage and allocate the department's budget.
5. Set and enforce data quality standards and compliance.
6. Align data initiatives with other department heads.
7. Address and resolve major challenges faced by the team.
8. Decide on third-party vendor partnerships.
9. Drive the long-term vision for data engineering in the company.

QUALIFICATIONS & SKILLS:

1. Bachelors in Data Science, Computer Science, Engineering, Statistics and 10+ years of relevant experience.
2. Minimum 10+ years of managerial experience utilizing strong leadership skills, including mentoring, communication, decision-making, and project management.
3. Knowledge of the Big Data technology landscape beyond the buzzwords; Experience with big data and analytics programming languages such as Python, R, Spark, NoSQL
4. Experience with batch and real-time processing frameworks (Hadoop, Apache Storm, Apache Kafka, Apache Spark etc.)
5. Proficiency working with DataFrames and writing complex SQL queries
6. Familiar with production Cloud / DevOps environments and Data lake, Data transformation, ETL/ELT, and other data concepts
7. Experience of leveraging MS/Azure ecosystem to manage the development and maintenance of cloud platform operations
8. A broad set of technical skills and knowledge across hardware, software, systems and solutions development
9. A proven track record of using quantitative analysis to impact key business or product decisions
10. Solid grasp of and experience with implementing and operating software development methodologies
11. A solid grasp of common statistical applications and methods (A/B tests and multivariate experiments, probabilities, regression)
12. Understanding of Agile Software Development Lifecycle and project planning/execution skills
13. Outstanding communicating skills with stakeholders at all levels, managing stakeholders' expectations and facilitate discussions across high risk or complexity or under constrained timescales.
14. Excellent Data analysis skills by helping teams apply a range of techniques for data profiling and sourcing system analysis from a complex single source with excellent capability to bring multiple data sources together in a conformed model for analysis
15. Outstanding capability to establish enterprise-scale data integration procedures across the data development life cycle and ensure that teams adhere to these. Able to manage resources to ensure that data services work effectively at an enterprise level.
16. Up to date with data innovation and expert in investigating emerging trends in data-related approaches, performing horizon-scanning for the organisation and introducing innovative ways of working.
17. Expert in Data integration design and can establish standards and well informed on best practice across different industries. The candidate can distinguish how to keep those standards up to date and ensures adherence to them.
18. Good understanding of concepts and principles of data modelling and can produce, maintain and update relevant data models for specific business needs. Also, shows good knowledge on how to reverse-engineer data models from a live system.
19. Expert in Metadata management and understands how metadata repositories can support different areas of the business. Capable of promoting and communicating the value of metadata repositories and knows how to set up robust governance processes to keep repositories up to date.
20. Expert in identifying and anticipating problems and know how to prevent them by linking how problems fit into the larger picture. Has the ability to identify and describe problems and help others to describe them and knows how to build problem-solving capabilities in others.
21. Expert in setting up team-based data engineering standards for programming tools and techniques and can select appropriate development methods. Act as an advisor on the application of standards and methods and ensure compliance and takes technical responsibility for all stages and/or iterations in a software development project, providing method-specific technical advice and guidance to project stakeholders.
22. Technical Expert in predicting and advising on data engineering future technology changes that present opportunities for a product or programme.
23. Experienced with reviewing requirements, specifications and defining test conditions with good understanding on how to identify issues and risks associated with work while being able to analyse and report test activities and results.
24. Experience in Agile Development, with specific Data Engineer/Data Architect (or similar) experience preferred; understands relationship with Product Owner, Agile coach, Data Scientist, Designer and rest of technical team.
25. Exceptional problem-solving skills: demonstrated ability to understand business challenges, structure complex problems, develop solutions
26. Ability to partner and influence key business stakeholders at all levels of the organization
27. Strong skills in team leadership and networking, ability to work across multiple organizations to accomplish diverse goals
28. Exceptional presentation, written, and verbal communication skills
29. Strong communication skills with ability to align the organization on complex technical decisions
30. Active coach and mentor whose goals are to grow and maximize the team's potential
31. Strong ability and enthusiasm around data strategy and ability to inspire team and organization around the usage of data

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Information Technology

Industries

Manufacturing

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