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

Smart Working; Milton Keynes; Hybride; GBP 90 000 - 120 000; Plein temps

Résumé du poste

Smart Working seeks a Lead Data Engineer to build and scale the data infrastructure powering an AI assistant platform. You will shape the data architecture, pipelines, and vector databases to enable reliable real-time analytics and ML workflows.

You will collaborate with AI and backend teams to ensure the platform processes large volumes of data efficiently. This role requires four days in the office weekly in Milton Keynes and offers a path to establish the data function as the company grows.

Qualifications

• 7+ years of professional experience in dedicated data engineering roles.
• Experience designing and building data pipelines and distributed data systems.
• Experience with relational databases (PostgreSQL preferred; MySQL acceptable).
• Experience with NoSQL databases.
• Strong Python programming.
• Ability to justify architectural decisions, not just implement.
• Experience building scalable backend systems.
• Experience designing data models and storage architectures.
• Understanding of data processing performance and optimisation.
• Desirable: Spark, Airflow, Kafka, Elasticsearch/OpenSearch.
• Desirable: PostgreSQL, MongoDB, vector databases (Qdrant/Milvus/pgvector).
• Desirable: Python data-processing libraries (Pandas/Polars).

Responsabilités

• Architect and build scalable data pipelines and infrastructure for AI and product systems.
• Design and maintain data ingestion, transformation and storage architectures for operational and AI workloads.
• Develop and manage batch and real-time data pipelines.
• Build and optimise systems for vector search and ML data pipelines.
• Ensure data reliability, security and governance across the platform.
• Collaborate with AI and backend teams to support training, inference and features.
• Implement monitoring, observability and data quality frameworks.
• Optimize performance of large-scale datasets and queries.
• Contribute to long-term data strategy and architecture decisions.
• Act as founding data hire and help shape data culture and hiring standards.
• Partner with founders and product leadership to translate data capabilities into product decisions.

Connaissances

Data pipelines

Distributed systems

PostgreSQL

NoSQL

Python

Backend systems

Data modeling

Spark

Airflow

Kafka

Elasticsearch/OpenSearch

Vector databases

Outils

PostgreSQL

MySQL

NoSQL databases

Python

Apache Spark

Apache Airflow

Kafka

Elasticsearch/OpenSearch

Qdrant

Milvus

pgvector

Pandas/Polars

Description du poste

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.

This role requires you to be in the office 4 days / week.

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.

This role requires you to be in the office 4 days / week.

Responsibilities

• Architect and build scalable data pipelines and infrastructure to support AI and product systems.
• Design and maintain data ingestion, transformation and storage architectures for operational and AI workloads.
• Develop and manage batch and real-time data pipelines.
• Build and optimise systems for vector search, retrieval and machine learning data pipelines.
• Ensure data reliability, security and governance across the platform.
• Collaborate with AI and backend engineering teams to support training, inference and product features.
• Implement monitoring, observability and data quality frameworks.
• Optimise the performance of large-scale datasets and query systems.
• Contribute to technical architecture decisions and long-term data strategy.
• Act as the founding data hire, defining culture, standards and the hiring bar for the data function as it scales.
• Partner directly with founders and product leadership to translate data capabilities into product decisions.

Requirements

• 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.

Nice to Have

• Experience working on AI or machine learning platforms.
• Familiarity with stream processing and event-driven architectures.
• Experience with cloud infrastructure such as GCP, AWS or Azure.
• Experience working in high-growth startups or early-stage companies.
• Experience with vector databases used in modern AI systems.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.