Senior Data Engineer

We are building the world’s first AI-native consumer law firm, and the data foundations underneath it have to match that ambition. Over the next 12 months, the data function will:

• Build out our data stack to enable ingestion, analysis and processing of a growing corpus of data, both to support BI and AI use-cases
• Drive data as a product with AI-native consumption so that anyone in the group can explore, dig into, and act on data without filing a ticket
• Build the integration playbook for law firms as we expand our firm portfolio. We need a canonical Lawhive data model that scales to all law firms and types of legal work

Our current stack: BigQuery, dbt, Hex, Dagster, K8s, Elementary, Claude Code, GCP and AWS infrastructure. We’ll look for strong opinions on best practices and technologies as we scale., 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., * 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

Do you have experience in SaaS?, * 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

Pulled from the full job description

• Annual leave
• Company pension, * 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!

Diversity at Lawhive

At Lawhive we know that diversity of thought is critical to delivering outlier outcomes. As such, we’re always working hard to ensure we build a diverse, inclusive team. We’re not yet where we want to be but as we scale we’ll only ever increase the focus we apply to this.

Compensation Range: £80K - £130K

About Lawhive

Our mission is to make the law accessible to everyone.

The legal industry is built on technology and processes that haven’t been updated in hundreds of years - that’s why we’ve reinvented the entire model from the ground up with our own bespoke AI operating system at the core.

Lawhive is a regulated law firm with an AI-native platform built to amplify expertise and revolutionise the way people practice law, leading to exceptional outcomes for clients and lawyers.

Lawhive Labs is how we bring this vision to life. It’s our frontier lab that combines top engineering, design, AI and legal talent from around the world, joining forces to build the future of law.

We’re backed by top-tier investors, including Google Ventures, Balderton Capital and TQ Ventures, and in December 2025, we secured $60M Series B funding round to facilitate international expansion and to grow our team.

We’re headquartered in London and in 2025 successfully launched in the US…and we’re just getting started.