Senior Data Engineer

Role Overview

• Design, build, and maintain scalable, reliable data pipelines across GCP and AWS infrastructure, with BigQuery as our warehouse
• Own and evolve our Dagster orchestration layer, ensuring pipelines are observable, testable, and operationally robust
• Architect and implement ingestion patterns for diverse source systems, from SaaS APIs to acquired firm data with unstructured schemas
• Define and enforce data quality standards at the ingestion layer: completeness, freshness, lineage, security, privacy and schema contracts
• Build the technical playbook for onboarding acquired firms’ data into Lawhive’s canonical data model
• Design repeatable ELT patterns that handle conflicting schemas, messy legacy systems, and varying data quality, making firm onboarding a weeks-not-months process
• Partner with Analytics Engineering on the canonical Lawhive data model, ensuring upstream pipelines deliver clean, well-structured data
• Enabling access controls and privacy-preserving access to firm tenanted data
• Apply LLMs and AI tooling (Claude Code, Cursor) to data engineering tasks: entity resolution, schema mapping, automated data quality checks, and pipeline generation
• Partner with our AI/ML teams to build reliable data pipelines that feed model training and inference workflows
• Building scalable storage and processing solutions for various data and AI projects and products
• Proactively monitor and optimise BigQuery usage for query performance and cost efficiency as data volumes grow
• Evaluate and recommend tooling changes to keep the stack modern, efficient, and fit for AI-native workflows
• Work closely with the Analytics Engineer and Data Analysts to ensure the platform supports self-serve analytics and the dbt semantic layer
• Partner with Product and Engineering to instrument new product features and surface clean event data
• Contribute to documentation and runbooks that make the platform accessible and understandable across the team.

Requirements

• 5+ years of data engineering experience, including hands-on ownership of production pipelines at a SaaS or tech scaleup
• Deep expertise in cloud data warehouses, ideally BigQuery, including performance tuning, partitioning, clustering, and cost management
• Comfortable with Python for pipeline development and have experience with orchestration tools (Dagster, Airflow, or similar)
• Built data integration patterns for complex or heterogeneous source systems. Bonus if in an M&A or multi-entity context
• Strong opinions on data modelling, pipeline design, and the modern data stack; able to defend trade-offs and push back on bad patterns
• AI-native in how you work. Use Cursor, Claude Code, or equivalent tools daily and think LLMs structurally change how data engineering gets done
• Collaborate effectively with Analytics Engineers and Analysts, understanding where the pipeline ends and modelling begins
• Commercially literate enough to translate business context into infrastructure decisions.

Tech Stack

• Airflow
• AWS
• BigQuery
• Cloud
• Google Cloud Platform
• Python

Benefits

• 💰 Meaningful early-stage equity at one of Europe’s fastest growing startups
• ✈️ 33 days’ annual leave (25 + bank holidays) plus your birthday off
• 💰 Pension contribution via Nest
• 💷 20% off legal fees through Lawhive
• 💻 Top-spec Macbook
• ⛳️ Regular team building activities and socials!