At a Glance
- Tasks: Design and maintain scalable data pipelines for an AI-native law firm.
- Company: Join Lawhive, a revolutionary legal tech startup backed by top investors.
- Benefits: Enjoy equity, 33 days off, a Macbook, and team socials.
- Other info: Diverse and inclusive team focused on innovative solutions.
- Why this job: Be at the forefront of transforming the legal industry with cutting-edge technology.
- Qualifications: 5+ years in data engineering, expertise in BigQuery, and Python skills.
The predicted salary is between 60000 - 80000 £ per year.
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.
Data at Lawhive
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.
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 Bring
You’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!
- 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.
Senior Data Engineer employer: Lawhive
At Lawhive, we are committed to revolutionising the legal industry through innovative technology and a collaborative work culture. As a Senior Data Engineer, you will have the opportunity to shape the future of law while enjoying meaningful equity, generous annual leave, and a supportive environment that fosters professional growth and diversity. Join us in our London headquarters and be part of a dynamic team backed by top-tier investors, where your contributions will directly impact our mission to make law accessible to everyone.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Lawhive!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data Engineer at Lawhive.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Lawhive.
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We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Lawhive, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Lawhive. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Lawhive
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Lawhive!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
