About Aytza
Aytza is building the future of strategy consulting. We're an AI-native consulting firm that combines deep human expertise with the precision and speed of AI, designed from the ground up for healthcare leaders.
We turn the traditional consulting pyramid on its head: a core AI engine powers our data, analysis, and workflows, with humans in the loop to guide judgment and quality. This lets experienced advisors support clients in a leaner, faster, and more scalable way. We are post-product and revenue-positive, actively serving a fast-growing group of clients navigating complex, high-stakes decisions across the healthcare landscape.
The Role
We're hiring a Senior Data & Insights Engineer to build and evolve the data asset at the center of Aytza's engine.
Roughly 80% of this role is building: designing the schemas and data models that make our datasets reusable, constructing and maintaining the pipelines that feed them, and steadily moving us toward a data environment that largely sustains itself, documented, tested, and reliable enough that it does not need constant attention to stay trustworthy.
The remaining 20% is analysis. We want someone who truly understands the data rather than just the infrastructure beneath it: someone who can interrogate a dataset, judge whether it actually supports the question being asked, spot what's missing or misleading, and explain what it means in business terms.
Much of what we work with is messy, real-world data from inconsistent sources. A large part of the job is structuring it well so it can be reused with confidence across engagements.
You'll work directly with our leadership and consulting teams on both their in-engagement, real-time needs and the downstream reuse of what you build.
This is a remote role with the expectation of flexibility to collaborate during East Coast hours and occasional travel for in-person team time. We're a distributed team, but strong real-time collaboration matters at our stage.
Note: we cannot sponsor a US work visa.
Where We Are Technically
We've built a set of core internal-facing products that:
- Follow defined workflows to seek, capture, analyze, deliver, and summarize the key datasets that inform strategic decision-making for healthcare companies.
- Apply best-in-class methodologies to synthesize and produce multi-format outputs that support guided client work.
- Standardize the consulting workflow end-to-end, including client onboarding, analysis and deliverable creation, and client collateral.
Our data work runs on Python and SQL, with ingestion pipelines, document processing, and retrieval feeding the products our consultants use every day. For context on the wider environment: those products are built in TypeScript and React and run on AWS, and our AI systems use LLM APIs from Anthropic and others. You would live primarily in the data layer, with colleagues who own the rest. We expect this stack to keep evolving as the product and customer needs evolve.
What You'll Do
- Own and evolve our core data asset: the schemas, data models, and structures that determine whether a dataset can be reused across engagements or has to be rebuilt each time.
- Design and maintain the pipelines that acquire, transform, and refresh our data: ingestion from inconsistent external sources, transformation logic, orchestration, and incremental updates that keep things current without manual intervention.
- Turn messy real-world data into something trustworthy: normalization, entity resolution, deduplication, and clear provenance, so that anyone using a dataset knows where it came from and how much weight it can carry.
- Build toward a self-sustaining data environment: documented schemas and a working data dictionary, automated data-quality checks that catch problems before our consultants do, reproducible pipelines, and enough self-serve access that routine questions do not require you.
- Do real analysis, not just plumbing. Interrogate datasets, answer substantive questions from the consulting team, assess whether a source is fit for the purpose it is being used for, and communicate findings in business terms.
- Partner with our leadership and consulting teams on live engagement needs and on the longer-term question of which data assets are worth investing in.
- Structure data for AI and LLM consumption: retrieval, structured outputs, and the unglamorous data shaping that determines whether an AI workflow produces something reliable.
- Hold a high bar on quality: code review discipline, sensible testing, and documentation clear enough that the team can understand, maintain, and extend what you build.
What You'll Bring
Must-Haves
- 4+ years in data engineering, analytics engineering, or a closely related role, including ownership of a data asset.
- Strong backend programming, database design, and pipeline skills: Python and SQL, schema and data modeling, ETL/ELT, medallion architecture, orchestration, and incremental refresh. You should be able to design the structure, not just populate it.
- Strong analytical ability: You can interrogate a dataset, judge whether it supports the question being asked, notice what is missing or misleading, and explain the business implications. Understanding the data matters here as much as understanding the systems that carry it.
- A track record with messy, real-world data: inconsistent sources, poor documentation, duplicate and conflicting records. You have turned that kind of input into something durable and reusable, and you know when a dataset is not yet trustworthy enough to rely on.
- Comfort working directly with non-technical experts: translating a consultant’s question into data work, and translating what you find back into plain language they can act on.
Preferred
- Experience inside a consulting firm or professional-services environment, ideally in a data role.
- Experience in healthcare, digital health, or life sciences.
- Hands-on work with LLM APIs or AI-powered products, retrieval, embeddings and vector stores, or structuring data for LLM consumption.
- Familiarity with modern data tooling: dbt, orchestration frameworks such as Airflow, Dagster, or Prefect, and warehouses such as Snowflake, BigQuery, Redshift, or Postgres.
- Data quality, testing, or observability tooling, and experience introducing it somewhere it did not previously exist.
- Document processing, search, web data acquisition, or unstructured-to-structured extraction.
- Experience building internal tools, research tools, or analytics products for expert users.
- Experience working with permissioned data, client data, auditability, or regulated-adjacent workflows.
Why Aytza
You'll build data infrastructure on a business that is already live, already generating revenue, and already used every day by a highly skilled internal team. That means no waiting around for user feedback; our consultants are sophisticated, engaged, and vocal, which creates an exceptionally tight and productive build-learn-iterate loop.
It's also a rare setup if you want your data work to visibly matter. The assets you build are what our advisors reason from in front of clients, so the quality of your modeling and your judgment about the data shows up directly in the quality of the advice. You'll be working at the intersection of AI, healthcare, consulting, and data, on a small team where what you ship matters immediately.
Compensation
Competitive salary commensurate with experience, depending on location and background.
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