5+ years of experience in building and managing production data pipelines from source to consumer.
Proficient in Python and advanced SQL, with skills in writing and optimizing complex queries.
Deep expertise in data modeling using Postgres or similar relational databases, including schema design and migrations.
Experience in parsing and normalizing messy, real-world data with inconsistent formats.
Hands-on experience with workflow orchestration for multi-step data pipelines.
Familiarity with AWS data services such as S3 and managed databases.
Strong testing mindset for data validation and quality checks.
Responsibilities
Own the design and production of data pipelines, ensuring quality and freshness targets are met.
Collaborate with ML engineers to provide necessary features and datasets for models.
Design resilient pipelines that can handle failures and recover from bad inputs.
Implement monitoring to catch data issues before they affect customers.
Manage costs effectively as data volumes increase, selecting appropriate tools.
Contribute to setting data standards and shaping the platform as an early team member.
Benefits
Real stakes with actual customers, impacting revenue directly.
Opportunity to work on a product with existing paying customers and proven outcomes.
Chance to define a new category in agentic software for physical infrastructure.
Work within a small, senior team that values open debate and quick decision-making.
Full Job Description
The role
We're looking for a Senior Data Engineer to build the data foundation behind Foresight.
You will build the pipelines that take complex project data from the tools our customers already use and turn it into clean, trusted, well-modeled data. That data powers our predictions, AI recommendations, analytics and customer reporting. This is a hands-on role on a new team, for someone who cares about data being correct, explainable and available when it matters.
At Foresight, data engineering is product engineering. Every number a customer sees, every forecast and every recommendation starts with data you made trustworthy. This could translate to work on:
Ingestion from industry tools: Build robust pipelines that bring in project data from the scheduling and planning tools the industry runs on, and handle every messy, inconsistent file along the way.
A reliable history of every update: Model data so each update is preserved exactly as it arrived, and teams can compare any two points in time with confidence.
Data that powers ML and AI: Build the features, training datasets and search indexes behind our predictions and AI recommendations, and keep them fresh as new data lands.
Analytics and reporting: Build the models and pipelines behind customer dashboards, stakeholder reports and exports, plus the internal analytics we run the business on.
Quality, isolation and governance: Build automated data-quality checks, lineage and access controls, so every customer's data stays accurate, isolated and in the region it belongs.
What you'll do
Own pipelines from design through production: define data contracts, set freshness and quality targets, and keep them met.
Partner with ML engineers on the features and datasets their models need, and with full stack engineers on the data behind each product feature.
Design for failure: pipelines that retry safely, can be re-run without duplicating data, and recover cleanly from bad inputs.
Make data observable, so problems are caught by monitors before a customer notices them.
Keep cost in check as data volumes grow, choosing the simplest tool that does the job well.
Help set the team's data standards and shape the platform as one of its first data engineers.
Requirements
You might be a fit if you
Have 5+ years of experience building and running production data pipelines, with ownership from source to consumer.
Are strong in Python and advanced SQL, including writing and tuning complex queries.
Have deep data modeling experience in Postgres or a similar relational database: schema design, indexing, partitioning and migrations.
Have experience parsing and normalizing messy real-world data: large files, inconsistent formats, missing or contradictory values.
Are hands-on with workflow orchestration for multi-step pipelines with dependencies, retries and backfills.
Have experience with AWS data services such as S3, managed databases, streaming and serverless compute.
Havea testing mindset for data: validation, quality checks, and reproducible, idempotent pipelines.
Are clear in communication and comfortable making pragmatic trade-offs on a small team.
Bonus points
Experience preparing data for ML: feature pipelines, training datasets, and keeping offline and online data consistent.
Experience with embeddings and vector search (pgvector or a search engine) for AI features.
Event-driven data: change data capture, streaming platforms, and the outbox pattern.
Familiarity with project scheduling data or tools from construction, engineering or energy.
Working with regulated enterprise or public-sector data: SOC 2, GDPR, data residency.
Infrastructure as code and CI/CD for data pipelines.
Early-stage or founding-team experience.
None of these are required. If the core of this role excites you, apply.
Benefits
Why join now
Real stakes, real customers. Real stakes, real customers. You won't be building for a hypothetical buyer. Owners and contractors are building now, on deadlines measured in revenue per day.
Blank canvas, solid ground. You get first-engineer level scope on a product that already has paying customers, real data and proven outcomes to tell.
Defining a category.Agentic software for physical infrastructure is a new category. What you build will become the standard.
Small, senior team. Flat, direct, low on process. We debate openly, decide quickly and ship.
Compensation and location
Total Comp: $180,000-240,000, based on experience, level, and location.
Benefits: Health, Dental, Vision, 401(k), PTO .
Location: New York, San Francisco, and Montreal are preferred. Occasional travel to may be required.
