Data Engineering for SaaS Startups.
Built to Scale With You.
Event pipelines, product analytics, multi-source integration — built for SaaS scale.
SaaS data engineering is different from generic data engineering. You are dealing with high-volume event streams from tools like Segment, product usage data locked in PostgreSQL, marketing metrics in HubSpot, and revenue data scattered across payment processors. We build the pipelines and warehouse that tie all of it together — so your product and analytics teams stop guessing and start answering real questions about retention, churn, and feature adoption.
Last updated: June 2026
5K+
Events per day processed through our SaaS event pipelines (based on FDE client engagement)
30+
Airflow pipelines built for one SaaS client across 4 data sources (based on FDE client engagement)
$12K
Annual infrastructure cost for a data platform serving 500 people (based on FDE client data)
Is This You?
This is for you if
- Your event tracking data from Segment or similar tools is not making it into usable analytics
- Your analysts are writing one-off queries against production databases because there is no warehouse
- You are scaling past the point where manual data pulls and spreadsheets work
- You need to consolidate product, marketing, and revenue data into one reliable source
- You have outgrown your current pipeline tools and need to migrate without breaking things
Not the right fit if
- You need a full-time embedded data engineer on your product team
- You only need a BI dashboard or Looker setup without underlying infrastructure work
- You are pre-product and do not have meaningful data volume yet
- Your primary need is data science or ML model building, not data engineering
What Does SaaS Data Engineering Include?
SaaS companies have specific data challenges that generic data engineers often miss. High-volume event streams, multiple SaaS tool integrations, and analytics teams that need self-serve access — all while keeping costs under control as you scale.
Event Tracking Pipelines
Your users generate thousands of events every day — clicks, sign-ups, feature usage, subscription changes. We build pipelines that collect, clean, and model that event data so your product team can actually answer questions like “which features drive retention” and “where do users drop off.” We have built event pipelines processing 5,000+ events per day on AWS for a SaaS company that was still running on legacy Pentaho.
Multi-Source Data Consolidation
Your product data lives in PostgreSQL. Marketing is in HubSpot. Sales is in your CRM. Support is in Freshdesk or Intercom. We connect all of it into a single warehouse where your team can run cross-functional analysis. In one SaaS engagement, we integrated 4 sources into 30+ Airflow pipelines that a growing team of 7 analysts depends on daily.
Legacy Tool Migration
If you are stuck on tools that worked at an earlier stage but cannot keep up now — Pentaho, Stitch, Fivetran at painful price points, or hand-rolled scripts — we migrate you to a modern stack without disrupting your existing data flows. We run systems in parallel during transition so nothing breaks.
Cost-Conscious Architecture
SaaS margins matter. We design data infrastructure that scales without scaling your bill proportionally. Smart data partitioning, efficient compute, and choosing the right tool for the job — not the most expensive one. One platform we built serves a 500-person organization for $12K per year in infrastructure costs.
How Does a SaaS Data Engineering Engagement Work?
We start by understanding your product, your data sources, and what your team actually needs. The architecture fits your business.
Map your data landscape
We audit every data source — product database, event tracking, CRM, marketing tools, support platforms — and identify what is connected, what is missing, and what is broken.
Build the foundation
Data warehouse, pipeline architecture, naming conventions, quality checks. Everything documented and version-controlled from the start.
Deliver and hand off
Working infrastructure, trained team, complete documentation. Your analysts and your next data hire can maintain and extend everything independently. Need us to keep monitoring pipelines or adding integrations as you scale? We do ongoing maintenance too.
See our full 5-phase process for details on compliance, risk management, and onboarding.
SaaS Data Engineering in Practice
Building an AWS Event Tracking Pipeline from Scratch: A SaaS Case Study
Flexible office marketplace SaaS · ~150 employees · Fast-growing, investment-backed · Full Pentaho to AWS migration
2 weeks
to get the pipeline live
5,000+ events/day
processed through the pipeline
Pentaho to AWS
legacy infrastructure modernised
Tech: AWS Lambda · AWS S3 · AWS Glue · PostgreSQL · Segment.io · Data Modelling · Data Partitioning · Cloud Infrastructure
Read case study →
Replacing a Legacy ETL Tool with AWS: A SaaS Migration Case Study
Flexible office marketplace SaaS · ~150 employees · Fast-growing, investment-backed · Full Pentaho to AWS migration
6 months
to fully replace Pentaho with a modern AWS stack
4 sources integrated
PostgreSQL, Pipefy, HubSpot, Segment.io
30+ pipelines
running in Airflow
Tech: Apache Airflow · AWS Lambda · AWS S3 · PostgreSQL · Pipefy · HubSpot · Segment.io · Python · SQL · Parquet · Data Modelling
Read case study →
From a Technical Leader
“The Fractional Data Engineer team worked with one of the world's largest retailers, building pipeline architecture with BigQuery, Airflow, and Looker. Strong technical execution, excellent planning, and clear communication with stakeholders. Would hire again.”
Let's Talk About Your Data Stack
Book a free call. We will review your current SaaS data setup and tell you what we would prioritize first.
Currently accepting 1 of 3 new clients
Frequently Asked Questions About SaaS Data Engineering
What data engineering do SaaS startups typically need?
Most SaaS companies need three things: event tracking pipelines to understand user behavior (from tools like Segment, Mixpanel, or Amplitude), a data warehouse that consolidates product, marketing, and revenue data, and reliable ETL/ELT pipelines connecting all their SaaS tools. The specific stack depends on your scale, but the pattern is consistent — most SaaS companies we work with are pulling data from 7 or more tools before we consolidate them.
Can you work with our existing Segment or analytics setup?
Yes. We have built event tracking pipelines on top of Segment.io, processing thousands of events per day. If your Segment data is messy or you are not getting value from it, we clean it up, model it properly, and connect it to your warehouse so your product and analytics teams can actually use it. In one engagement, we processed 5,000+ Segment events per day through our pipeline architecture.
We already have some pipelines but they keep breaking. Can you fix that?
That is one of the most common engagements we take on. We audit the existing setup, identify what is failing and why, then either fix the pipelines in place or rebuild them on a more reliable foundation. We do not rip and replace unless the current setup is genuinely unfixable.
How do you handle the migration from legacy tools without downtime?
We run old and new systems in parallel during the transition. Data keeps flowing through the legacy tool while we build and validate the replacement. Once the new pipelines are producing identical results, we cut over. In one engagement, we replaced an entire Pentaho infrastructure with AWS over 6 months without disrupting the analytics team.
Do you build product analytics dashboards?
We build the data layer that powers dashboards, not the dashboards themselves. That means clean, modeled data your analysts can query confidently, connected to tools like Metabase, Looker, or whatever BI tool your team prefers. We have set up Metabase for multiple clients and trained their teams to build reports on their own — product managers, marketing leads, and executives who had never touched a BI tool before.
What SaaS tools do you integrate with?
We are tool-agnostic. We have built pipelines pulling from Segment.io, HubSpot, Salesforce, Pipefy, Google Analytics, PostgreSQL, and many others. If your tool has an API or export mechanism, we can integrate it. We focus on the right architecture for your data, not on pushing specific vendors.
Related Reading
How to Build a Data Stack from Scratch
Step-by-step guide for teams starting with nothing.
Signs Your Data Stack Needs Rebuilding
How to tell when your current setup is holding your team back.
How to Set Up Apache Airflow for a Small Team
Practical Airflow setup guide for teams without a platform engineer.