Lead Data Engineer (Contract, Full-Time) [HR207]

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.
Experience with vector databases used in modern AI systems.
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.