Junior Data Engineer

Take your foundation in data engineering and machine learning to build a financial data warehouse from scratch for a fast-growing financial tech team, then own it long term. Real ownership, real scale, real impact you can point to.

**The Setup**

- Established real estate investment firm has a growing portfolio and outpacing the data infrastructure supporting it

- Core business already has a mature warehouse with data flowing seamlessly between systems. This hire builds the same thing for the affiliate

- Clinical care, staffing, budgets, and the systems running 41 communities, spread across nine-plus platforms today

- One system already feeds the warehouse, another is coming online next, the rest are yours to bring in

- Affiliate is growing fast on real demographic tailwinds and needs to build its own solutions instead of leaning on the parent company's infrastructure

**What You'd Build**

- A full data warehouse for the affiliate, bronze through gold, built for scale

- Pipelines moving data in and out, mirroring the two-way flow already live on the core business side

- Full-stack internal tools, React on Python APIs, putting trustworthy numbers in front of the people running dozens of communities

- Dashboards that shorten the distance between a question and a decision

- By year one, you understand the warehouse end to end

**Why This One's Different**

- Real operational data rarely arrives clean: a field means something different depending on which community entered it, a system update breaks an undocumented pipeline, a number looks off and has to be traced back to its source

- This isn't build-it-and-hand-it-off work. The bar is seeing around corners: not just shipping what was asked for, but catching where a stakeholder's request missed the actual problem

- You'll spend real time reverse-engineering undocumented relationships instead of building from a clean blueprint

- Thinking 6 to 12 months out, not just the immediate ask

**Who Fits**

- 1-4 years out of school with a foundation in data engineering or machine learning

- Comfort with messy, real-world data

- At least one meaningful pipeline or data/ML project, professional or independent, where you had to dig in and figure out what actually went wrong

- Can speak clearly about what broke and why

- Prior warehouse architecture experience is a plus, not a requirement. Neither is a name-brand pedigree

- Since resident health data flows through several of these systems, familiarity with HIPAA/PHI (or willingness to learn it fast) matters

- Genuine curiosity and real problem-solving instincts matter more than credentials

**Growth**

- Advancement tracks impact, not tenure. Strong performers take on scope quickly

- This warehouse becomes the operational backbone for a fast-growing senior housing platform riding a strong demographic tailwind, and you own it long term

**Stack**

Python, React, Snowflake, Airflow, dbt, SQL, AWS, Docker, CI/CD. Full-stack fluency isn't required at the start, the ability to learn fast is

**Logistics**

- Midtown Manhattan, 5 days/week in office

- Base salary + real bonus upside once you've proven out