👋Hello, we’re Instrumentl.

We’re a mission-driven startup helping the nonprofit sector to drive impact, and we’re well on our way to becoming the #1 most-loved grant discovery and management tool. ****

About us:

Instrumentl is a hypergrowth YC-backed startup with over 4,000 nonprofit clients, from local homeless shelters to larger organizations like the San Diego Zoo and the University of Alaska. We are building the future of fundraising automation, helping nonprofits to discover, track, and manage grants efficiently through our SaaS platform.

Our charts are dramatically up-and-to-the-right 📈 — we’re cash flow positive and doubling year-over-year, with customers who love us (NPS is 65+ and Ellis PMF survey is 60+). Join us on this rocket ship to Mars!

Senior Data Engineer

We’re looking for a Senior Data Engineer to help scale and evolve our data platform, which is in its early stages. You’ll play a key role in shaping the architecture, improving reliability, and building the systems that power data across the company. This is a high-impact opportunity to bring structure and scalability to an existing foundation—ideal for someone who enjoys improving systems, making architectural decisions, and driving best practices.

You’ll partner closely with engineering, product, and business teams to design and implement scalable systems for data ingestion, storage, processing, and analytics. This role is ideal for someone who enjoys high ownership, thrives in ambiguity, and wants to have a lasting impact on foundational infrastructure.

What you’ll do:

Design and scale our data platform including pipelines, models, and orchestration frameworks

Develop scalable ETL/ELT pipelines for ingesting data from APIs, databases, and event streams
Define and implement systems for data ingestion, storage, processing, and transformation
Build and manage workflow orchestration using tools like Airflow
Build semantic layer as well as dashboards
Establish best practices for data modeling, testing, and quality
Partner with stakeholders to shape data requirements and enable BI and analytics use cases
Optimize systems for performance, scalability, and cost from day one
Apply software engineering principles (testing, CI/CD, modular design) to data infrastructure

What you bring:

A min of 5+ years of software engineering experience with the last 2-3 years in data engineering, ideally including early-stage or 0→1 environments
Strong Python programming experience
Advanced proficiency in SQL
Proven experience building ETL/ELT pipelines end-to-end
Experience with orchestration tools like Airflow
Deep understanding of the data lifecycle: ingestion → storage → processing → transformation → serving
Experience with cloud platforms (AWS, GCP, or Azure)
Experience supporting BI tools (Looker, Tableau, etc.)
Familiarity with modern data warehouses (Snowflake, BigQuery, Redshift)

Nice to have:

Experience with streaming pipelines (Kafka, Kinesis)
Exposure to ML/AI data pipelines
Familiarity with Ruby and Rails
Experience with data analytics and data science concepts
Experience working in startup environments

Instrumentl

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