Data Engineer

RES
Kings Langley
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Description

Do you want to work to make Power for Good?We're the world's largest independent renewable energy company. We're guided by a simple yet powerful vision: to create a future where everyone has access to affordable, zero carbon energy.We know that achieving our ambitions would be impossible without our people. Because we're tackling some of the world's toughest problems, we need the very best people to help us. They're our most important asset so that's why we continually invest in them.RES is a family with a diverse workforce, and we are dedicated to the personal professional growth of our people, no matter what stage of their career they're at. We can promise you rewarding work which makes a real impact, the chance to learn from inspiring colleagues from across a growing, global network and opportunities to grow personally and professionally.Our competitive package offers a wide range of benefits and rewards.The positionThe Data Engineer will play a crucial role in designing, developing, testing, and maintaining data solutions.The primary responsibility for this role will be to ensure the efficient flow of data across systems, enabling data-driven decisions making and insight generation. They partner with the business and departmental specialists to support, troubleshoot, and implement features and functionality of the applications in their areas of responsibility. The data engineer provides efficient and consistent use of data technologies throughout RES Americas and coordinates with other IT functions across the globe to ensure standardization of support and processes.AccountabilitiesProvides timely and quality resolution support through troubleshooting, research, and resolution. Assist in planning and consulting on how to effectively deliver designs, models, and ETL pipelines within the organization. Develops and designs ETL pipelines, contributes to logical data models, and coordinates with users the development and delivery of reporting and integration solutions. Assesses and improves the operational health of the data solutions including security, availability, performance, interoperability, and reliability. Implements data governance policies and security to ensure compliance and safe guard data assets. (HIPPA, GDPR, GAAP) Partners with functional leaders to identify strategic objectives, determine functional needs and requirements, and craft comprehensive plans for continuous data solution development aligned with those goals and needs. Independently tests, debugs, and documents enhancements to the data platform. Authors and routinely reviews technical documentation necessary to facilitate usage and adoption of applications, including user and admin guides; routinely review documentation to ensure accuracy. Identifies trends in customer issues, recommends improvements, and implements approved improvements. Writes custom reports and SQL queries. Presents and communicates with management on the evaluation of the current solution, proposed solution, and the optional and recommended next steps.Additional ResponsibilitiesMentors and coaches' other application support resources within their area of expertise. Assumes responsibilities for additionally assigned assignments related to supported data engineering solutions. Presents, supports, and leads by example with a safety and quality-oriented attitude. Ability to assess and work with the business to obtain business requirements and understand the business needs. Attends work regularly and punctually, as scheduled or expected. Complies with Employee Handbook, Code of Conduct, and Company Policies & Procedures.Knowledge, Skills & AbilitiesDemonstrated experience in developing and maintaining enterprise-level data solutions. Experience in developing solutions for Business Intelligence reporting. Knowledge of Data modeling methodologies Inmon/Kimbal. Proficient in SQL and Python. Knowledge of software development lifecycle. Experience troubleshooting issues and optimizing performance in a cloud environment. Experience with Microsoft Synapse, SQL Server, Azure Data Factory and related technologies. Knowledge of help desk ticketing software, preferably ServiceNow. Proficient in Microsoft Office. Familiarity with ITIL v3 or related service delivery frameworks. Demonstrated experience in Project Management as a team member. Exceptional verbal and written communication. Critical thinking skills. Self-motivated and able to work under pressure.

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.