Up to 65KJob Purpose
Design, build, and maintain the infrastructure and systems that allow the UIS Data Science team to collect, store, process, and analyse large amounts of data. Design and implement data pipelines, integrating data from various sources, and ensure the data is accurate, complete, and accessible to those who need itYour place in the organisation
Unconventional Industrial Solutions is part of the Customer Solutions team which, in turn, is part of the Growth Division.Customer Solutions
Our Mission Is To Make Energy Affordable And Accessible To Everyone - Enabled By Providing Our Clients With 5 Key Capabilities
The Customer Solutions team is part of the company’s Growth Division. Our team of consultants, technology experts, data scientists, developers and engineers support our customers as they embrace digital technology and transform their organization.
Responsibilities And Duties
- Digital Advisory services & Human-Centric Service Design
- End-to-End Digital Twins as a foundation for smart advisory
- Take advisory services & breakthrough analytics solutions to the next level by productizing them and serving them to our clients & ecosystem through user-centric B2B marketplace
- Sustainability solutions
- Autonomous solutions design and implementation
The day-to-day responsibilities of the data engineer will vary depending on the specific project being worked on but will typically include:
Skills And Experience
- Collaborating with data scientists, analysts, front end developers and other stakeholders to understand and meet their data needs.
- Designing, building, and maintaining the data infrastructure and pipelines that support data-driven applications and analytics.
- Managing and monitoring data pipeline performance and troubleshooting any issues that arise.
- Work with a wide variety of data types and sources including structure, unstructured, semi-structured and sensor data
- Optimising and scaling data storage and processing systems.
- Ensuring data quality and consistency across different systems and platforms.
- Developing and implementing data security and privacy controls.
- Quickly build prototypes and proofs-of-concept to prove value of propositions
- Collaborating with other teams in Customer Solutions: Working with other teams within the organisation, such as Solutions Factory (DevOps and data systems), PMO, and project teams. Understand customers’ challenges, agree and deploy solutions.
- Management: coaching and line-management responsibilities subject to experience and team structure
- Demonstrate deep understanding of the complexities and nuances of structured and unstructured industrial data, and how to prepare it for analysis
- Make full use of technology to automate data pipelines and build analytical warehouses
- Deep understanding of cloud-based data platforms (Azure SQL DB, Azure Synapse, ADLS, AWS, Hadoop, Spark, Snowflake, No-SQL etc).
- Proficient scripting in programming languages such as Java, Python, Scala
- Expert in SQL
Applied Analysis and Insight
- Good basic understanding of the main types of ML model with reference to the required data structures, formats and hygiene.
- Au fair with how current machine learning tools and platforms (Python, IBM, Knime, Github, R, Spark, Weka, Amazon ML, Azure ML etc) integrate as part of the analytical ecosystem
- Experience with the tools used to validate, monitor and deploy ML models (Tensorflow, ML Flow, AWS Studio, Docker, Kubernetes etc).
- Understand enterprise-wide business-processes, IT systems and data
- Designs data flows and platforms that enable businesses to execute long term data strategy
- Work with data scientists and data owners throughout a project to ensure data is interpreted correctly and solutions are viable
- Experience in processing of IOT/Sensor Data to solve problems in an industrial setting
- Build relationships with key technical and senior stakeholders across the business
- Spend time with customers to fully understand the challenges and the nuances of their organisation and data.
- Be comfortable working in Agile project-management frameworks.
Qualifications And Experience
- Ensures that analysts have ready access to clean, accurate and properly-documented data
- Able to interrogate data to understand its quality and distribution, informing required transformations
Undergraduate/Masters degree in STEM or other numerate subjects. Plus 3 or more years relevant working experience.
PhD in a relevant field (Computer Science, Industrial Analytical Systems etc) plus one year commercial experience