Data Engineer - Python & Azure

Birmingham
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

DATA ENGINEER - PYTHON & AZURE

Contract type: Permanent

Hours: 37.5

Salary: Circa £61,000, depending on experience

Location: Birmingham or Leeds

WFH policy: Employees are required to attend the office 2 days/week

Travel: You will be required to travel to the London office once a month (this is expensed by the business)

Flexible working: Variety of flexible work patterns subject to line manager discretion e.g. Compressed 9-day fortnight.

Reports to: Data Engineer Manager

Deadline Note: We reserve the right to close the advert before the advertised deadline if there are a high volume of applications.

Role Summary:

As the Data Engineer, you will own LCCC’s data ecosystems and will work with developers, solution architects, technical BAs, analysts, data scientists, and other SMEs to define the optimum data platform for the business. You will build the required ETL/ELT pipelines for the ingesting, processing and storing of data and ensure data integrity, accuracy, and reliability throughout the data lifecycle.

You will have a specific focus on developing the data inputs that are utilised by the organisation’s internal forecasting model of the GB power markets and operations; designing a framework that scales effectively along with the source data.

Key Responsibilities:

  • Build and maintain ETL/ELT pipelines to make data accurate and easy to use

  • Work to ingest and transform data sets from a variety of data sources including APIs, web scraping, backup databases and third-party services

  • Analyze data to identify patterns, anomalies, and structure in preparation for Extract, Transform, and Load (ETL) processes

  • Ensure efficient data synchronization and flow between various platforms and systems

  • Explore ways to enhance data quality and reliability

  • Assist with the establishment of a data culture across the organisation

  • Drive better data governance through the creation and embedding of principles and processes e.g. Flow Diagram, Data Dictionary, Data Semantic Layers

  • Set service level indicators and monitor execution of data workflows and configure alerts Identify data quality issues through data profiling, analysis and stakeholder engagement

    Skills Knowledge and Expertise

    Essential:

  • At least 1 year of data engineering experience - building data platforms and supporting modelling and data analytics

  • Hands on experience within the Azure Data ecosystem, with Azure Databricks, Data Factory, Data Lake, and Synapse. Certifications are a plus.

  • Strong competence in Python, ideally with PySpark experience

  • Strong competence in SQL

  • Experience building and maintaining data pipelines

  • Experience managing DevOps Pipelines

  • Strong experience in process optimisation, performance tuning, data modelling and SQL/database design skills.

    Desirable:

  • Experience in data architecture

    Employee Benefits:

    As if contributing to and supporting work that makes life better for millions wasn’t rewarding enough, we offer a full range of benefits too. Key benefits that may be available depending on the role include:

  • Annual performance based bonus, up to 10%

  • 25 days annual leave, plus eight bank holidays

  • Up to 8% pension contribution

  • Financial support and time off for study relevant to your role, plus a professional membership subscription

  • Employee referral scheme (up to £1500), and colleague recognition scheme

  • Family friendly policies, including enhanced maternity leave and shared parental leave

  • Free, confidential employee assistance, including financial management, family care, mental health, and on-call GP service

  • Three paid volunteering days a year

  • Season ticket loan and cycle to work schemes

  • Family savings on days out and English Heritage, gym discounts, cash back and discounts at selected retailers

  • Employee resource groups

    About Low Carbon Contracts Company

    The Low Carbon Contracts Company (LCCC) exists to help decarbonise the generation of electricity and make it more affordable for the future. Our work is central to the delivery of the Government’s objective to achieve Net Zero target by 2050.

    Please take the time to answer the optional diversity questions

    At LCCC, we are dedicated to fostering a diverse and inclusive workplace where everyone can be their authentic selves and contribute to our mission of advancing a flexible energy future. Our aim is to be reflective of the environments where we operate and truly benefit from a rich tapestry of backgrounds and experiences where everyone thrives which of course make us stronger together. Your diversity data is valuable to us, it helps us understand whether we are effectively connecting with underrepresented groups and realising our diversity aims. Please note that your diversity data will remain anonymised to us as it only feeds into high-level reports not connected to the candidates

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.