Lead Data Engineer
Lead Data Engineer at Trust In Soda: first senior data hire designing and scaling the data infrastructure behind LightWork AI's Felicity platform for AI-powered property management. Day to day: architecting batch and real-time pipelines, vector search, and ML data workflows with Python, PostgreSQL, and modern data tooling, while setting standards and growing the team.
- Compensation
- Not disclosed
- Location
- London, United Kingdom
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- 7+ years in data or backend engineering
- Experience designing and building data pipelines and distributed data systems
- Relational databases: PostgreSQL preferred; MySQL or similar acceptable
- Experience with NoSQL databases
- Experience with vector databases used in AI systems
- Strong Python programming
- High ownership
- Strong problem-solving and architectural thinking
- Comfortable in a fast-moving startup
Nice to have
- Apache Spark
- Apache Airflow
- Kafka
- Elasticsearch/OpenSearch
- MongoDB
- Vector databases: Qdrant, Milvus, pgvector
- Python libraries: Pandas, Polars
Full description
Overview
Lead data engineer responsible for designing and scaling LightWork AI’s data infrastructure powering Felicity. You will define data architecture, build real-time pipelines, vector search, and ML data workflows in collaboration with AI and backend teams. As the first senior data hire, you’ll set engineering standards and help grow the data team while shaping the platform for high growth. This role offers ownership and the chance to influence a category-defining AI platform for property operations. Join a founder-led, fast-moving startup aiming to transform property management with AI.
Pay / Benefits
- Competitive salary + equity
Responsibilities
- Architect and build scalable data pipelines and infrastructure to support AI and product systems
- Design and maintain ingestion, transformation, and storage architectures for operational and AI workloads
- Develop and manage batch and real-time data pipelines
- Build and optimize vector search, retrieval, and ML data pipelines
- Ensure data reliability, security, and governance
- Collaborate with AI and backend engineering teams to support training and inference
- Implement monitoring, observability, and data quality frameworks
- Optimize performance of large-scale datasets and query systems
- Contribute to long-term data strategy and technical architecture decisions
- Mentor and help build the future data engineering team
Key requirements
- 7+ years in data or backend engineering
- Experience designing and building data pipelines and distributed data systems
- Relational databases: PostgreSQL preferred; MySQL or similar acceptable
- Experience with NoSQL databases
- Experience with vector databases used in AI systems
- Strong Python programming
- Desirable: experience with frameworks/infrastructure like Apache Spark, Apache Airflow, Kafka, Elasticsearch/OpenSearch; databases PostgreSQL, MongoDB; vector databases (Qdrant, Milvus, pgvector); Python libraries Pandas, Polars
- high ownership
- strong problem-solving and architectural thinking
- comfortable in a fast-moving startup
- Python
- PostgreSQL
- MySQL