At a Glance
- Tasks: Lead the design and ownership of our data platform, ensuring robust data systems.
- Company: Join a forward-thinking tech company focused on innovation and collaboration.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on continuous learning and development.
- Why this job: Shape the future of data engineering and make a real impact in AI-driven solutions.
- Qualifications: Proven experience in data architecture, Python, and machine learning systems.
The predicted salary is between 80000 - 100000 £ per year.
We are looking for someone who goes beyond building pipelines and focuses on designing durable, well-architected data systems.
Architectural Mindset: Proven experience as a Tech Lead, Principal Engineer, or System Architect designing and owning complex, distributed systems.
Strong Software Engineering Foundations: A software-engineer-first mindset with deep experience in Python and production-grade engineering practices. Experience with libraries such as Pandas or Polars is expected, but architectural thinking matters more than specific tools.
Machine Learning Exposure: Hands-on experience working with machine learning systems and tooling (e.g. Hugging Face, feature stores, model inference pipelines, or similar), with an emphasis on enabling ML in production rather than research experimentation.
Database & Storage Expertise: Advanced SQL skills and hands-on experience with modern cloud data warehouses (e.g. Snowflake or equivalent), alongside solutions for unstructured or semi-structured data.
ETL/ELT & Orchestration: Experience designing and operating modern data pipelines using tools such as dbt, Airflow, or equivalent orchestration and transformation frameworks.
Engineering Rigor: Deep experience with Git-based workflows, CI/CD pipelines, automated testing, and maintaining long-lived systems in production.
Engineering Judgement: Demonstrated ability to make and defend trade-offs—when to model data, when not to ingest data, and how to balance correctness, performance, and cost.
Analytical Depth: Ability to interrogate and analyse data directly to validate system behaviour and ensure high levels of data quality.
(Desirable) Experience with Analytics-as-Code platforms such as Looker/LookML.
(Desirable) Experience building internal platforms that enable, rather than directly deliver, BI and reporting.
(Desirable) Experience with automation platforms such as n8n for connecting operational systems.
(Desirable) Experience designing systems for multimodal data (text, images, video, documents).
What the job involves: We are looking for a Tech Lead - Data Engineering to serve as the primary architect and owner of our data platform. Reporting to the Head of Engineering, you will own the end-to-end technical direction of our data ecosystem and act as the most senior individual contributor in this domain. This role sits at the intersection of data engineering and system design.
You will define how data is ingested, modelled, stored, transformed, and exposed across the company, with an emphasis on robust pipelines, clear data contracts, and reliable operation at scale. The large volumes of transactional data we generate form the foundation for machine learning and other AI-driven solutions that we are actively building and evolving. Your focus will be on designing and evolving data systems that are reliable, maintainable, and fit for long-term use, applying strong software engineering principles to how data is structured, integrated, and operated at scale.
Own the Data Platform: Take end-to-end ownership of the data platform, including ingestion, storage, transformation, and exposure layers. This includes setting technical direction and being accountable for system reliability, performance, and cost.
System Architecture: Lead the design of distributed data systems, ensuring clean integration between backend services, external APIs, event streams, and data storage layers.
ML-powered Product Enablement: Work closely with product and engineering teams to design and lead data foundations for machine-learning-powered product features, ensuring data quality, traceability, and production readiness.
Data Modelling & Strategy: Act as the lead architect for data models and contracts. Design schemas for both structured and unstructured data, balancing flexibility, performance, and long-term maintainability.
Engineering Standards & Artefacts: Set and uphold engineering standards across the data domain. Produce and maintain architecture diagrams, design documents, and Architecture Decision Records (ADRs). Champion best practices including version control, CI/CD, modular design, backwards compatibility, and automated testing.
Pipeline & ETL/ELT Design: Architect and implement high-scale, fault-tolerant data pipelines. Make deliberate trade-offs around latency, freshness, cost, and complexity, selecting fit-for-purpose tools rather than defaulting to trends.
Hands-on Delivery: Spend a significant portion of your time building and maintaining core pipelines, schemas, and services in production. This is a hands-on role with direct responsibility for critical systems.
Technical Leadership: Define the technical roadmap for data, perform deep code reviews, and mentor engineers on system design, SQL, and Python.
Workflow Automation: Design and implement automated workflows (using tools such as n8n or custom Python services) to bridge operational gaps and reduce manual processes.
Governance & Security: Design enterprise-grade governance frameworks covering access control, data lineage, observability, and data integrity.
Production Ownership: Be accountable for production incidents, data quality issues, and cost regressions within the data platform.
Data Engineering Lead employer: Plentific
As a Data Engineering Lead, you will thrive in a dynamic and innovative environment that prioritises engineering excellence and collaborative problem-solving. Our company fosters a culture of continuous learning and growth, offering ample opportunities for professional development while working on cutting-edge data systems that drive impactful machine learning solutions. Located in a vibrant tech hub, we provide a supportive atmosphere where your contributions are valued, and your expertise can shape the future of our data ecosystem.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineering Lead
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at meetups. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your projects, especially those involving data pipelines and machine learning. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios related to data engineering. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Data Engineering Lead
Some tips for your application 🫡
Show Off Your Architectural Mindset:When writing your application, make sure to highlight your experience in designing complex data systems. We want to see how you've taken ownership of projects and made architectural decisions that led to robust solutions.
Emphasise Your Software Engineering Skills:Don’t forget to showcase your software engineering foundations! Mention your deep experience with Python and any libraries like Pandas or Polars. We’re looking for a software-engineer-first mindset, so let that shine through.
Talk About Your Machine Learning Exposure:If you’ve worked with machine learning systems, give us the details! Share your hands-on experience with tools like Hugging Face or model inference pipelines, especially how you’ve enabled ML in production environments.
Be Clear and Concise:Keep your application clear and to the point. Use bullet points where necessary to make it easy for us to read. And remember, applying through our website is the best way to get your application in front of us!
How to prepare for a job interview at Plentific
✨Know Your Data Systems Inside Out
Make sure you can discuss your experience with designing and owning complex data systems. Be ready to explain how you've approached architectural challenges in the past, focusing on durability and scalability.
✨Showcase Your Software Engineering Skills
Prepare to demonstrate your software engineering foundations, especially in Python. Bring examples of production-grade engineering practices you've implemented, and be ready to discuss libraries like Pandas or Polars in the context of your architectural thinking.
✨Highlight Your Machine Learning Experience
Be prepared to talk about your hands-on experience with machine learning systems. Discuss how you've enabled ML in production environments, rather than just research, and share specific tools you've used, like Hugging Face or feature stores.
✨Discuss Your Approach to Data Quality
Emphasise your analytical depth by explaining how you validate system behaviour and ensure high levels of data quality. Share any experiences where you had to make trade-offs between correctness, performance, and cost in your data pipelines.
