Data Engineer

St. James's Place
gb
10 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

St. James's Place (SJP) works inpartnership to plan, grow and protect our clients’ financial futures. Wedeliver personalised, face-to-face financial advice to our clients, who trustus to manage their money to reach their goals. We provide this service via thePartnership, a network of qualified, expert advisors. We put financialwellbeing and responsible business choices at the heart of everything we do. Webelieve in the value of difference and know that diverse teams can help usproblem solve and innovate for clients.

We look for people to join SJP to make animpact and to contribute to our culture which is based around long termrelationships, doing the right thing, and being the best version of ourselves.

Location: London - Paddington

Workplace Type: Hybrid

Employment Type: Permanent 

Seniority: Mid-Senior Level

We are seeking an experienced Data Engineer to join the Investment Management Division at St James’s Place. Our approach to investment decision making and manager selection is differentiated from many of our peers. We place significant emphasis on evidenced based insights relating to manager and strategy behaviour, rather than historical performance or industry standard reporting metrics.

This is a key role within the SJP Equities team, with responsibility to ensure that we have the necessary data in place to support our approach. It will require the development of robust and structured processes as well as creative problem solving and innovation in the development of those capabilities.

If you are a data driven professional with a passion for investment management, we would love to hear from you.

What you’ll be doing?

Working as a part of the Equities data and Analytics team in creating Robust ETL processes for data collection from internal and third party sources with audit and config. Building automated processes for data cleansing / manipulation Data storage & mapping in databases ( Snowflake) Building automated processes for data transfers from financial analytics systems ( FactSet) to databases( Snowflake) and vice versa. Systems infrastructure management and Access controls

Who are we looking for?

Practical knowledge of SQL and Python, capable of using complex joins & functions to transform data to suit end user needs in Snowflake data platform. Familiarity with Snowflake, Matillion, Azure DevOps, Microsoft Fabric, Visual Studio Code. Exposure to Power BI and/or Tableau or equivalent reporting tool. Development Experience using Excel VBA Macros and power query to manage and maintain existing tools built using Excel. Ability to seek improvements & efficiencies above and beyond suggestions from the team, calling on previous experience and industry best practice

What's in it for you?

Private Medical paid for by Company. Meaningful protection benefits with real value, such as 10X life cover, PHI, and critical illness.* Non-Contributory Pension – 10% (increasing with length of service up to 15%) with further pension matching. Parental leave – 6 months full pay 28 days holiday entitlement plus bank holidays (based on full-time equivalent) with the option to buy up to an additional 5 days holiday

*Not applicable to Fixed-Term Contracts (standard uplift applies in lieu of the protection benefits)

Flexible Working
We know that everyone works best indifferent ways, at different times and in different environments. We haveintroduced a hybrid working policy to provide greater flexibility for part-timework, job-sharing, remote working, and flexibility on hours. Our people areencouraged to work in a flexible way that suits their lifestyle, so please askthe question and start a conversation!

Research tells us that applicants(especially those from underrepresented groups) can be put off from applyingfor a role if they do not meet all the criteria or have been on an extendedcareer-break. If you think you would be a good match for this role and candemonstrate some transferable experience please apply, regardless of whetheryou tick every box.

Reasonable Adjustments
We're anequal opportunities employer and want to ensure our recruitment process isaccessible and inclusive for all, if you require reasonable adjustment(s) atany stage please let us know by emailing us at

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.