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 excellent career advancement opportunities.
- Why this job: Shape the future of data engineering and make a real impact in AI-driven solutions.
- Qualifications: Proven experience in data architecture, strong Python skills, and a passion for system design.
The predicted salary is between 80000 - 100000 £ per year.
Requirements
- 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 at our innovative company, you will thrive in a dynamic work culture that prioritises collaboration and continuous learning. We offer competitive benefits, including professional development opportunities and a supportive environment that encourages creativity and technical excellence, all while being located in a vibrant area known for its tech community. Join us to make a meaningful impact on our data ecosystem and advance your career in a forward-thinking organisation.
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 related to data engineering. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios. Think about how you’d design a data pipeline or tackle a system architecture challenge—be ready to discuss your thought process!
✨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 owned projects and made architectural decisions that led to robust solutions.
Emphasise Your Software Engineering Skills:Don’t forget to showcase your strong software engineering foundations! Mention your experience with Python and any libraries like Pandas or Polars, but also focus on your overall engineering mindset and practices.
Demonstrate Your Machine Learning Exposure:If you’ve worked with machine learning systems, let us know! Share specific examples of how you’ve enabled ML in production environments, as this is a key aspect of the role we’re looking to fill.
Keep It Clear and Concise:Make sure your application is easy to read and straight to the point. Use clear language to describe your experiences and skills, and don’t hesitate to apply through our website for a smoother process!
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 strong 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.
✨Talk About Machine Learning Experience
Highlight any hands-on experience you've had with machine learning systems. Discuss how you've enabled ML in production environments, rather than just research settings, and be prepared to talk about specific tools you've used.
✨Be Ready for Technical Leadership Questions
As a potential Tech Lead, expect questions about your approach to mentoring and leading teams. Think about how you've set engineering standards in the past and be prepared to share your thoughts on best practices in data engineering.
