Data Engineering built for Cybersecurity companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For Cybersecurity companies, this means navigating SOC 2, ISO 27001 while shipping fast.
Who needs Data Engineering for Cybersecurity?
- Detecting threats in real time at scale
- False positive rates undermining analyst trust
- Integrating across fragmented security tool stacks
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Security products are held to a higher engineering bar than almost any other software category.
We work with Cybersecurity leaders in to plan and deliver Data Engineering without the usual delivery surprises.
Talk to our Cybersecurity Data Engineering specialists this week
Book a free 30-minute discovery callCommon Data Engineering mistakes we see across Cybersecurity teams in
- Detecting threats in real time at scale
- False positive rates undermining analyst trust
- Integrating across fragmented security tool stacks
- Meeting compliance certifications for enterprise sales
- Keeping pace with evolving attack techniques
How we de-risk Data Engineering for Cybersecurity teams in
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:
- SOC 2
- ISO 27001
- NIST frameworks
- GDPR
Proven Results
"We helped a cybersecurity startup reduce false positive alert rates by 37% with an improved detection pipeline."
Read the full case studyBring your Data Engineering idea to a team that ships for Cybersecurity
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 cybersecurity projects addressing challenges like detecting threats in real time at scale. We helped a cybersecurity startup reduce false positive alert rates by 37% with an improved detection pipeline.
For cybersecurity clients, we pay close attention to SOC 2, ISO 27001, 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 cybersecurity clients include detecting threats in real time at scale and false positive rates undermining analyst trust. We tailor our discovery process to surface the specific version of these problems your business faces.