(15h Left) Data Engineer (Mid/Senior/Lead)

BJSS
Milton Keynes
11 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst Placement Programme

Data Analyst Placement Programme

Data Analyst Placement Programme

Data Analyst Placement Programme

Data Analyst Placement Programme

Data Analyst Placement Programme

About UsWe’re an award-winning innovative techconsultancy - a team of creative problem solvers. Since 1993 we’vebeen finding better, more sustainable ways to solve complextechnology problems for some of the world’s leading organisationsand delivered solutions that millions of people use every day.Inthe last 30 years we won several awards, including a prestigiousQueen’s Award for Enterprise in the Innovation category for ourEnterprise Agile delivery approach.Operating from 26 locationsacross the world, we bring together teams of creative experts withdiverse backgrounds and experiences, who enjoy working and learningin our collaborative and open culture and are committed toworld-class delivery.We want to continue to grow our team withpeople just like you!About the RoleWere building out our DataEngineering practice across multiple levels. Depending on yourexperience and aspirations, you could be contributing as a key teammember, leading a dedicated team, or taking on principal engineerresponsibilities across multiple teams and larger strategicprojects. While the contractual job title will be Data Engineer,the role and responsibilities will be tailored to your experiencelevel and our organisational needs.We are DataOps advocates and usesoftware engineering best practices to build scalable, re-usabledata solutions to help clients use their data to gain insights,drive decisions, and deliver business value. Clients engage BJSS totake on their complex challenges, looking to us to help deliverresults against their business-critical needs which means we get towork with a wide range of tools and technologies and there arealways new things to learn.BJSS Data Engineers are specialistsoftware engineers that build, optimise, and maintain dataapplications, systems and services. This role combines thediscipline of software engineering with the knowledge andexperience of building data solutions in order to deliver businessvalue.As a BJSS Data Engineer, you’ll help our clients deploy datapipelines and processes in a production-safe manner, using thelatest technologies and with a DataOps culture.You can expect toget involved in a variety of projects in the cloud (AWS, Azure,GCP), while also gaining opportunities to learn about and use dataservices such as Databricks, Data Factory, Synapse, Kafka,Redshift, Glue, Athena, BigQuery, S3, Cloud Data Fusion etc.AboutYouYoure an engineer at heart and enjoy the challenge of buildingreliable, efficient data applications systems, services, andplatformsYou have a good understanding of coding best practices anddesign patterns and experience with code and data versioning,dependency management, code quality and optimisation, errorhandling, logging, monitoring, validation, and alertingYou haveexperience in writing complex queries against relational andnon-relational data storesStrong proficiency in Python programming,with a solid understanding of object-oriented programming (OOP)principles, best practices, and a commitment to writing clean,maintainable, and well-tested codeExperience using Python dataprocessing libraries for large-scale data manipulation, cleaning,and analysis, with a preference for PySpark over PandasFamiliaritywith one or more data platform technologies such as Databricks,Snowflake, and MS Fabric. We have a preference for Databricks, butwelcome applications from candidates with otherexperiencesExcellent SQL skills, including the ability to writecomplex queries, optimise query performance, and design efficientdatabase schemasSome of the PerksFlexible benefits allowance – youchoose how to spend your allowance (additional pensioncontributions, healthcare, dental and more)Industry leading healthand wellbeing plan - we partner with several wellbeing supportfunctions to cater to each individuals need, including 24/7 GPservices, mental health support, and otherLife Assurance (4 xannual salary)25 days annual leave plus bank holidaysHybrid working- Our roles are not fully remote as we take pride in the tight knitcommunities we have created at our local offices. But we offerplenty of flexibility and you can split your time between theoffice, client site and WFHDiscounts – we have preferred rates fromdozens of retail, lifestyle, and utility brandsAn industry-leadingreferral scheme with no limits on the number of referralsFlexibleholiday buy/sell optionElectric vehicle schemeTrainingopportunities and incentives – we support professionalcertifications across engineering and non-engineering roles,including unlimited access to O’ReillyGiving back – the ability toget involved nationally and regionally with partnerships to getpeople from diverse backgrounds into techYou will become part of asquad with people from different areas within the business who willhelp you grow at BJSSWe have a busy social calendar that you canchoose to join– quarterly town halls/squad nights out/weekends awaywith families included/office get togethersGymFlex gym membershipprogrammePlease note: any applicants must be able to gain valid SCclearance.

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.