Data Engineering built for LegalTech companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For LegalTech companies, this means navigating Attorney-client privilege requirements, GDPR while shipping fast.
Who needs Data Engineering for LegalTech?
- Document review consuming excessive billable hours
- Confidentiality and privilege requirements in software design
- Integrating e-discovery and case management tools
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Legal teams need software that respects confidentiality while finally automating the repetitive parts of practice.
We specialize in Data Engineering for LegalTech, with a growing track record across industries that move fast.
Start your Data Engineering project with a team that knows LegalTech
Book a free 30-minute discovery callCommon Data Engineering mistakes we see across LegalTech teams
- Document review consuming excessive billable hours
- Confidentiality and privilege requirements in software design
- Integrating e-discovery and case management tools
- Inconsistent contract review processes
- Client portals lacking modern usability
How GarudLabs approaches Data Engineering for LegalTech companies
Data warehouse architecture (Snowflake, BigQuery)
ETL/ELT pipeline development
Data quality monitoring and alerting
BI dashboard integration
Data governance and access controls
Core Technology Stack
SnowflakeBigQueryAirflowdbtPythonKafkaAWS Glue
Timeline, investment & compliance
Typical timeline
8-16 weeks
Typical investment
$25,000 - $140,000
Data Engineering engagements in this combination typically need to account for:
- Attorney-client privilege requirements
- GDPR
- Bar association data handling rules
- SOC 2 compliance expectations
Our Approach
LegalTech software fails quietly if it isn't built for document review consuming excessive billable hours from the start. We scope for Attorney-client privilege requirements and GDPR requirements up front, and keep the review cadence tight enough to catch drift before it ships.
See our case studiesStart your Data Engineering project with a team that knows LegalTech
Get a free project cost estimateFrequently Asked Questions
Most Data Engineering engagements take 8-16 weeks, depending on scope and how clear the requirements are upfront. We'll give you a firm timeline estimate after a short discovery call, not a generic range.
Data Engineering projects with us typically range from $25,000 - $140,000, scaled to the complexity of your requirements. We provide a detailed, itemized estimate before any work begins so there are no surprises mid-project.
For data engineering, we typically work with Snowflake, BigQuery, Airflow, dbt, chosen based on your specific scalability and integration requirements. We're not tied to a single stack and will recommend the right tools for your situation rather than our default.
A typical data engineering engagement includes data warehouse architecture (snowflake, bigquery), along with the other deliverables outlined in our proposal, such as documentation and a post-launch support window. The exact scope is tailored to your project during discovery.
Most clients come to us for data engineering because of data scattered across disconnected systems, among other related challenges. We start every engagement by mapping your specific pain points before writing a single line of code.
We've delivered multiple legaltech projects addressing challenges like document review consuming excessive billable hours. We helped a legal services platform cut contract review time by 45% with an AI-assisted review workflow.
For legaltech clients, we pay close attention to Attorney-client privilege requirements, GDPR, building these requirements into the architecture from day one rather than retrofitting them later. We'll confirm which specific regulations apply to your business during discovery.
The most common challenges we solve for legaltech clients include document review consuming excessive billable hours and confidentiality and privilege requirements in software design. We tailor our discovery process to surface the specific version of these problems your business faces.