Lead Data Engineer - Founding Member (Contract, Full-Time) [HR208] (UK)

About Smart Working

At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity - it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well-being. Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally.

Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.

About the Role

We are looking for a Lead Data Engineer to build and lead the data infrastructure powering an intelligent AI assistant  platform. This role will architect and scale the systems that power our AI products, from real-time data pipelines and analytics infrastructure to vector databases and machine learning data workflows.

You will work closely with AI engineers, backend engineers, and product teams to ensure our platform can process large volumes of operational data reliably and intelligently. You will define our data architecture, tooling, and engineering standards, and play a key role in building the foundations of the future data team.

• 7+ years of professional experience, with the majority of that experience in dedicated data engineering roles.
• Strong experience designing and building data pipelines and distributed data systems.
• Experience working with relational databases, with PostgreSQL preferred, although MySQL or similar is acceptable.
• Experience working with NoSQL databases.
• Strong programming experience in Python.
• Demonstrated ability to make and justify architectural decisions, rather than only implementing them.
• Experience building scalable backend systems.
• Experience designing data models and storage architectures.
• Strong understanding of data processing performance and optimisation.
• Experience with some of the following data frameworks and infrastructure technologies is highly desirable: Apache Spark, Apache Airflow, Kafka, and Elasticsearch or OpenSearch.
• Experience with relevant database technologies is highly desirable, including PostgreSQL, MongoDB, and vector databases such as Qdrant, Milvus or pgvector.
• Experience with Python data-processing libraries such as Pandas or Polars is highly desirable.