lawhive - Senior Data Engineer
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Requirements
• 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 • 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
Responsibilities
• 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
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! • 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.
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