Senior Data Engineer

Prompt is a mission-driven startup looking for an experienced Senior Data Engineer to help push forward an ambitious set of data initiatives in healthcare, with a particular emphasis on making data actionable to drive decision making.

Prompt delivers highly automated and modern B2B enterprise software to rehab therapy businesses, their teams, and most importantly the patients they serve. We’ve established ourselves as the go-to platform in the space and are rapidly growing our market share by delivering software people love.

As we scale, data is becoming increasingly central to how we make decisions and drive improvement. We’re looking for a Sr. Data Engineer who can operate broadly across the team, with a product-focused mindset in building and maintaining data pipelines and data transformations from complex data sources. Comfort developing hypotheses, modeling datasets, and analyzing that data to guide client and internal decision making is essential. This role will contribute to internal and external facing data and analytic products – helping drive extraordinary outcomes for our clients and their patients while internally driving efficiency and decision-making across Product, Client Success, Growth, and Operations.

This is a high-ownership role focused on iteratively building systems, partnering closely with stakeholders, and ensuring our data products deliver real value.

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  Drive Prompt’s mission to improve healthcare through high-quality, actionable data that informs client and internal decision-making

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  Own the design, development, and iteration of complex data systems — from raw data ingestion through transformation, modeling, and downstream analysis

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  Perform exploratory analysis and hypothesis-driven investigations to surface insights and guide both client-facing and internal decision-making

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  Leverage modern AI and LLM-powered tools (e.g., code assistants, agents, automation frameworks) to accelerate data transformation, analysis, and iteration — while maintaining high standards for reliability, security, and maintainability

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  Develop and evolve well-modeled datasets, complex transformations, and metrics

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  Design and implement data quality checks, monitoring, and observability to ensure correctness, freshness, and trust in data products

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  Partner with stakeholders across the company to understand client and internal workflows, define success criteria, and deliver data products to drive improvement

• 4+ years of experience building data systems, including data pipelines, transformations, and analytical datasets

• Strong experience designing, building, and operating end-to-end data workflows—from raw data ingestion through transformation, modeling, and downstream analysis

• Proven ability to work with complex, messy, and evolving data sources and turn them into reliable, well-modeled datasets

• Experience performing exploratory and hypothesis-driven analysis to inform product, operational, or client-facing decisions

• Strong judgment around data quality, correctness, observability, and maintainability in production data systems

• Strong proficiency in Python and SQL for data transformation, analysis, and pipeline development

• Experience leveraging modern AI and LLM-powered tools (e.g., code assistants, agents, automation frameworks) to accelerate data engineering and analysis workflows

• Strong problem-solving mindset and ability to operate effectively in ambiguous, fast-paced environments

• Interest in a hands-on, high code-contribution role; this is not an engineering management position, though growth opportunities exist over time

• Experience with complex healthcare datasets – including patient care, operations, and billing data

• Hands-on experience with analytics engineering tools such as dbt for data transformations and metabase for building analytics dashboards (or other data transformation and dashboarding tools)

• Experience working in AWS (e.g., S3, Athena, RDS, Redshift, DMS, Glue, ECS, Lambda, or similar services)

• Experience working with relational databases and warehouses (e.g., PostgreSQL, Athena, Redshift, Snowflake, BigQuery)

• Experience building internal or client-facing analytics products used by multiple teams or customers