Senior Data Engineer

What You'll Do

• Optimize and improve Filevine's production usage of Snowflake and Cortex features — including warehouse management (usage, sizing, monitoring, etc.), clustering, query performance tuning, cost governance, and storage efficiency.
• Own and evolve our agentic data modeling and natural language data retrieval (text-to-sql) capabilities: build and curate semantic models, refine prompts, expand verified question libraries, and measure answer quality so that natural-language analytics get more accurate over time.
• Design and build batch and streaming data pipelines that ingest, transform, and model data from Filevine's product, CRM, billing, and telemetry systems into trusted, well-documented data products.
• Build the data foundations that power agentic AI workflows and LOIS — including feature pipelines, retrieval datasets, and low-latency serving paths for LLM-based reasoning over customer data.
• Establish reliability and governance standards including data quality checks, lineage, monitoring, incident response, access control, and PII handling consistent with our compliance posture.
• Partner with product and engineering stakeholders to define event contracts, model business concepts (matters, firms, users, billing) consistently, and reduce ambiguity across downstream consumers.
• Lead the evaluation and adoption of emerging tools across the modern data stack, recommending right-fit solutions that align with Filevine's strategic and security goals.
• Provide technical mentorship within the Data Engineering team, contribute to code reviews and design documents (DDs/ADRs), and help raise the bar on data engineering practice at Filevine.
• Participate in on-call rotations to maintain SLAs for production data pipelines and analytics surfaces.

What You'll Need

Required

• 5+ years of professional data engineering or backend engineering experience, with a proven track record of delivering production-grade data systems that drive measurable business outcomes.
• Significant hands-on experience operating a modern cloud data warehouse in production (e.g., Snowflake, BigQuery, Redshift, Databricks, Synapse, or equivalent) — including performance tuning, warehouse and cost management, role-based access control, and orchestration of warehouse-native compute (stored procedures, UDFs, streams/tasks, or equivalent).
• Demonstrated experience building with Agentic AI or LLM-powered systems in production — e.g., RAG pipelines, tool-using agents, MCP servers, warehouse-native LLM functions (such as Snowflake Cortex, BigQuery ML, or Databricks AI), or comparable frameworks.
• Expertise in advanced SQL and Python for building reliable, well-tested data pipelines and transformations.
• Experience with modern data modeling and transformation tooling such as dbt, including testing, documentation, and backward-compatible model design that supports self-service analytics.
• Experience with workflow orchestration (Airflow, Dagster, or similar) and cloud-native deployment on AWS, Azure, or GCP.
• Strong fundamentals in data modeling (dimensional, star/snowflake schemas), distributed systems, performance tuning, and data quality / observability principles.
• Professional experience with modern software development methodologies: Agile/Kanban, Git, CI/CD, and DevOps.
• Excellent written and verbal communication skills, with the ability to explain complex technical and data concepts to both technical and non-technical stakeholders.
• B.S., M.S., or Ph.D. in Computer Science, Information Systems, Engineering, or a related field — or equivalent professional experience

Nice to Have

• Hands-on Snowflake experience, including Snowpipe, streams/tasks, data sharing, and cost/governance tuning at scale.
• Experience with Snowflake Cortex Analyst specifically, including authoring and iterating on semantic models and verified queries.
• .NET / C# experience, or familiarity with reading and integrating against a .NET-based application backend.
• Experience using modern UI development tools, particularly Svelte or React
• Experience supporting machine learning workflows: feature stores, training datasets, or real-time scoring infrastructure.
• Experience in SaaS or product-led growth environments, including product analytics and revenue/usage telemetry.
• Infrastructure-as-code experience (Terraform), containerization (Docker, Kubernetes), and deployment (Octopus).
• Familiarity with the legal tech domain, document-heavy data, or working with unstructured data at scale.
• Track record of mentoring engineers and contributing to hiring and team-building.

What You Can Expect

• You will be a core builder of the data and AI foundations that LOIS and Filevine's product surfaces are built on.
• Your work will directly shape how legal professionals query, reason over, and act on their data — and will determine how fast, accurate, and trustworthy our agentic AI experiences become.