Lead Data Engineer

Midnite
Liverpool
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer - Azure Synapse

Why Midnite?


Midnite is a next-generation betting platform that is built for today’s fandom. We are a collective of engineers and designers who all share a passion for building the best sportsbook & casino experience possible, allowing our fans to feel closer to the games they love through the rush of winning money.


Unlike the alternatives, Midnite doesn't feel like a website built two decades ago. Instead, it's a cutting-edge creation, designed and constructed from the ground up with the latest technologies. Crafting an experience that's truly intuitive, immersive, and immediately understandable is no walk in the park, but we thrive on the challenge. We believe we're on the brink of creating something truly awesome.


What will you do?


We’re looking for a Lead Data Engineer to drive the next phase of our data strategy at Midnite. This is a hands-on leadership role where you’ll set the technical direction, own the design and scalability of our data infrastructure, and ensure the team delivers high-quality, impactful solutions.


You’ll work across the full data lifecycle from ingestion and modelling to orchestration, monitoring, and analytics enablement, while also mentoring engineers and shaping engineering best practices. As a lead, you’ll partner with our leadership team to make sure our data function not only delivers but also drives strategic decision-making.


Our Tech Stack:


Python, Docker, Dagster, dbt, Fivetran, Apache Iceberg, Snowflake, S3, Glue, ECS, and Omni. We’re constantly evolving our stack and welcome input from engineering leaders on how we can improve scalability, reliability, and efficiency.


Leadership & Collaboration:


As Lead Data Engineer, you’ll be both a technical expert and a team leader. You’ll:


  • Set technical standards and drive adoption of best practices across the team.
  • Mentor and coach engineers, raising the bar on quality and delivery.
  • Collaborate closely with senior stakeholders to align data initiatives with business priorities.
  • Champion innovation, evaluating new tools, platforms, and methodologies.


Responsibilities:


  • Own the technical strategy for data engineering, ensuring our stack scales with the business.
  • Design, maintain, and evolve robust data pipelines and architecture to support low latency batch use cases.
  • Oversee the implementation of data models and frameworks that support analytics, and business intelligence.
  • Drive engineering best practices across testing, monitoring, version control, and automation.
  • Lead code reviews, enforce quality standards, and ensure technical debt is managed proactively.
  • Manage and mentor engineers, supporting career development and creating a culture of excellence.
  • Stay ahead of industry trends, introducing tools and methods that future-proof the data platform.


Essential Experience:


  • 7+ years in data engineering, with at least 2+ years in a lead or equivalent role.
  • Proven track record of designing and scaling data platforms in a high-growth or start-up environment.
  • Strong expertise in Python and SQL, with deep experience in orchestration frameworks (Dagster, Airflow, Prefect).
  • Advanced knowledge of data modelling and architecture (Kimball dimensional modelling, Data Vault etc).
  • Hands-on experience with dbt, modern data warehouses, and AWS.
  • Demonstrated ability to mentor and develop engineers.


Desirable Experience:


  • Experience with Snowflake.
  • Experience with Apache Iceberg.
  • Experience with infrastructure-as-code (Terraform preferred).
  • Experience embedding observability and monitoring in data systems.
  • Previous experience building and leading data teams in a scale-up environment.


What’s in it for you:


  • Shape our future: Play a key role in our team's success, where your voice matters, and you'll have a direct impact on shaping Midnite's future.
  • Connect and unwind: Take part in our quarterly gatherings where our community comes together to bond and have fun.
  • Comprehensive health coverage: Look after your well-being with our outstanding zero-excess health insurance plan, which includes optical and dental coverage.
  • Simplify life: Take advantage of our nursery salary sacrifice scheme, allowing you to conveniently pay your child's nursery fees straight from your paycheck.
  • Work-life balance: Enjoy 25 paid holidays a year, plus generous paid maternity, paternity, and adoption leave, supporting you during life's most important moments.
  • Productive home office: We provide everything you need for a comfortable and ergonomic home setup, ensuring you're as productive as possible.
  • Flexible working: We embrace flexible working, allowing you to adjust your schedule when life's unexpected moments arise.
  • Latest tech made easy: With our salary sacrifice schemes, you can upgrade to the latest gadgets, household items, and mobile tech without the upfront cost.
  • Exclusive perks: Enjoy a wide range of discounts on retailers, groceries, and subscriptions, making life a little more affordable.
  • Grow with us: Expand your skills through internal and external learning opportunities while benefiting from access to mentorship programs that support your development.
  • Transparent compensation: We provide competitive pay with clear team bandings and salary grids, ensuring that salary discussions are simple and fair.
  • Constructive feedback: We foster a transparent culture, encouraging individual feedback and review sessions to help everyone improve.
  • Work from anywhere: Whether it's a cosy cottage in the Cotswolds or anywhere else, enjoy the freedom of working remotely.

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.