Senior Data Engineer II

As Apartment List's data platform grows across more domains, more pipelines, and more

stakeholders, the architectural decisions made today determine how much technical debt we're

paying down in a year. We're looking for a Senior Data Engineer II (IC4) to own that

architecture; end-to-end pipeline design, platform investment tradeoffs, and the technical

judgment calls that keep the system reliable as it scales.

This is not a design-from-the-whiteboard role. You'll still be hands-on-keyboard: writing

pipelines, debugging production issues, and shipping code alongside the team. What sets this

role apart is scope; you'll make architectural decisions independently, influence how Analytics

Engineering, Data Science, and Engineering partners build on the platform, and be the person

other data engineers come to when a design decision needs a second opinion. You'll work

closely with Analytics Engineering, Data Science, and Engineering partners to shape how the

data platform evolves.

Here's what you'll do as part of the team

• Own and evolve data pipeline architecture across core domains — ingestion, transformation, modeling, and serving — making project-level architectural decisions independently and evaluating tradeoffs between freshness, cost, scalability, and simplicity.
• Lead the design and implementation of platform-level improvements: warehouse cost management, compute efficiency, and access control architecture, treating reliability, observability, and cost efficiency as core design constraints rather than afterthoughts.
• Identify and lead technical initiatives that improve the platform's long-term health —proactively surfacing investments (orchestration, CI/CD, data access, developer experience) before they become blockers, and making the case for them.
• Drive large, technically complex projects or multiple concurrent medium-sized initiatives that span teams, taking responsibility for outcomes rather than just execution.
• Lead monitoring and testing strategy for your domain: proactively close observability gaps across the org, build alerting ahead of failures, and serve as the go-to engineer for the hardest production issues.
• Influence technical decisions and architectural direction beyond the Data & Analytics team, partnering directly with EPD stakeholders on infrastructure decisions that affect their roadmaps.
• Actively mentor other data engineers and analytics engineers, reviewing architectural and modeling decisions, and operate as a technical peer to senior engineers across teams.
• Integrate AI meaningfully into data engineering workflows — building tooling and automation that creates leverage for the whole team, not just individual output, and coaching others on effective, validated use.