Original listing text, shown exactly as published by the company.
The Role
As Senior Data Engineer, you’ll be working on the infrastructure backbone of Lawhive’s data platform. You’ll own the pipelines, orchestration, and data integration work that powers everything from self-serve analytics to AI product features. Reporting to the Head of Data, you’ll work closely with our Analytics Engineer, Data Analysts, and Engineering teams to build a platform that scales with the business.
This role is for someone who is hands-on, opinionated about infrastructure, and energised by complexity, whether that’s integrating a newly acquired firm’s messy data or rearchitecting a pipeline to handle 10x the data.
What You’ll Do
Data Infrastructure & Pipelines
- 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
Acquisition Data Integration
- 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
AI-Native Engineering
- 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
- Set a high bar for how data engineering gets done in an AI-native organisation
Platform Scalability & Performance
- Building scalable storage and processing solutions for our 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
Cross-functional Partnership
- 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
What You’ll BringYou’ll be a great fit for this role if
- You have 5+ years of data engineering experience, including hands-on ownership of production pipelines at a SaaS or tech scaleup
- You have deep expertise in cloud data warehouses, ideally BigQuery, including performance tuning, partitioning, clustering, and cost management
- You’re comfortable with Python for pipeline development and have experience with orchestration tools (Dagster, Airflow, or similar)
- You’ve built data integration patterns for complex or heterogeneous source systems. Bonus if in an M&A or multi-entity context
- You have strong opinions on data modelling, pipeline design, and the modern data stack; you can defend trade-offs and push back on bad patterns
- You’re AI-native in how you work. You use Cursor, Claude Code, or equivalent tools daily and think LLMs structurally change how data engineering gets done
- You collaborate effectively with Analytics Engineers and Analysts, understanding where the pipeline ends and modelling begins
- You’re commercially literate enough to translate business context into infrastructure decisions
Nice-to-haves
- Experience with dbt
- Familiarity with K8s for data workloads
- Background at a PE-backed software rollup or M&A-heavy company
- Exposure to legal services, legal tech, or regulated marketplaces
Interview process
- Introductory call with our Talent team
- 1:1 with our CTO
- Technical Assessment
- Values interview with our Founders
- We offer!
