Lead Data Engineer

OpenCredo
London
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer (Azure)

Lead Data Engineer / Architect – Databricks Active - SC Cleared

OpenCredo (OC) is a UK based software development consultancy helping customers achieve more by leveraging modern technology and delivery approaches. We are a bunch of passionate technologists who thrive tackling complex challenges, delivering pragmatic and sustainable solutions for our customers. Curious, tenacious but always sensitive to our customers' context, we are not afraid to speak our minds to help steer our customers towards understanding and achieving their key goals.

We are looking for a Lead Data Consultant to help us grow and on our journey of taking OC to the next level. Your experience as a data focused technologist and leader will involve helping teams and companies of various shapes and sizes pragmatically think, strategise, and deal with the ingestion, processing, integration and ability to get insights out of their data.

What you’ll spend your time on:

  • Data Strategy & Architecture: Help customers make sense of, and understand their data in the context of what their business is trying to achieve. Create data models and articulate the tradeoffs around different data approaches. Design and Architect complex data platforms and solutions across a variety of different scenarios.
  • Leadership & Consulting: Provide technical leadership and guidance for our customers and your fellow team-mates in the area of data and data platforms.
  • Hands On Delivery: Design, Build and Develop modern data architectures which deliver value and provide end-to-end data solutions. Create scalable data processing pipelines and platforms, and productionise machine learning models (MLOps).
  • Mentoring, Educating & Growing: Advise, educate and help grow customers and colleagues alike on various data related topics. Guide, teach and provide space for OCers to learn their craft, and advance their careers.
  • Pre Sales Activities: Assist with creating and providing input into proposals, as well as technically liaising and representing OC in pre-sales calls.
  • Public Speaking (optional). Develop and deliver talks at international conferences, meetups and within the broader community (Help provided)

Requirements

What we’re looking for:

  • Customer facing data expert: You will be comfortable advising and guiding customers, confident in your ability to deliver as a technical Data Subject Matter Expert.
  • Broad range of data expertise across areas such as data strategy, data governance, data architecture, data privacy & security, data lineage, data modelling, cloud data offerings, master data management (MDM) etc
  • Pragmatic & Versatile Architect Skills: You’re someone who is equally happy coding, whiteboarding and articulating approaches & solutions with customers, or leading a team. You understand core data & architectural principles, yet always seek to blend and balance real world considerations with pure theoretical approaches.
  • Distributed Systems Experience: You have developed and worked with Big data architectures including delivering event driven solutions. You are aware of the fallacies of distributed computing and how this impacts and relates to complex data platforms and storage solutions.
  • Innovation & Continuous Learning: You enjoy and actively seek to learn about new technologies and techniques in the Data and AI/ML space. You are an advocate for promoting and incorporating best practices around data and general good software practices into the broader delivery process.

Proven real-world/project experience of at least 3 of the following:

  • Relational databases/data warehousing (e.g Oracle, DB2, SQL Server, Sybase, Snowflake)
  • Document databases (e.g. Mongo, ArangoDB, Couchbase, Solr)
  • Big Data (e.g. Hadoop ecosystem, Bigtable)
  • Data streaming (e.g. Kafka, Flink, Pulsar, Beam, Spark)
  • Cloud databases (e.g. Snowflake, CockroachDB)
  • Other database genres (e.g. Graph, Columnar, time series)

Benefits

We’ll give you…

  • A highly competitive basic salary
  • 5% matched contributory pension
  • Private Health Insurance
  • Life Insurance
  • 25 days holiday plus public holidays (plus an extra day for each year of service)
  • Cycle to work scheme
  • A high-spec laptop (of course!)


Need more reasons? Heres are few more...

  • Work with some of the most exciting new technologies
  • Spark off co-workers who’ll challenge your thinking and help you to achieve your potential
  • Deal openly and honestly with customers
  • Benefit from a transparent environment including regular company meetings where we discuss anything and everything
  • Have exceptional opportunities as a speaker, blogger and contributor to open source projects. We have some great connections in the wider technology community that we encourage our team to make the most of!
  • Work alongside senior leaders who understand and value passionate technologists;
  • Enjoy coming to work! We’re a friendly, sociable bunch who genuinely support each other and have a lot of fun

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.

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

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

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

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.