Senior Data Engineer

Description

About DoorLoop

DoorLoop is property management software built for speed and the smart choice for people who take growth seriously. With offices in Miami, New York City, and Tel Aviv, we’re a global company helping property owners and managers move faster, scale smarter, and get real support, real fast.

We’re proudly People First. That’s why we’re a Certified Great Place to Work, recognized by Forbes as one of America’s Best Startup Employers in both 2024 and 2025, and earn top ratings on Glassdoor.

Mission

DoorLoop is looking for a Senior Data Engineer to co-own our data platform as its second engineer. Part of the foundation is in place: a layered dbt project on Snowflake, a semantic layer that defines our core business metrics once, and an AI data assistant that answers business questions from it. Much of it is still being designed, and you will help build it. You will take design decisions with our founding data engineer, own whole business domains end to end, and raise the bar on modeling, reliability, and review as more of the company depends on the platform. You will also build data products with AI: our AI data assistant, internal dashboards, and the data apps that come next, built AI-first and tested so their answers can be trusted.

You will work directly with Finance, GTM, and CX leaders on the metrics that run the business, and with analysts who ship their own dbt changes, helping them do it well.

This role is for an experienced engineer who wants to shape the foundation while it is still being built: open-minded, willing to push back on the right questions, and ready to make design decisions and own them.

Responsibilities

• Own core business domains end to end (GTM, CX, and Finance), from the stakeholder conversation to the model, the metric, and the dashboard or AI answer built on it

• Help build the foundation: take design decisions with the founding engineer on modeling patterns, incremental strategy, ingestion design, semantic-layer structure, and warehouse cost

• Lead metric-definition work with business leaders: settle contested definitions, document them, and get sign-off

• Own platform reliability: testing strategy, monitoring and alerting, the incident process, and root-cause fixes

• Design and build ingestion for new sources, choosing the right pattern (Fivetran, Airbyte, or custom n8n → S3 → Snowpipe pipelines)

• Reconcile financial data (Stripe payments and payouts) to its source of truth, and own the checks that keep revenue numbers right

• Extend the semantic layer: definitions, descriptions, and verified queries, so every tool gives the same answer

• Build data products with AI: the AI data assistant that answers business questions, internal dashboards, and new data apps. Design them AI-first, and build the evaluation sets that prove their answers are correct

• Set the standard for code review, and mentor the analysts who ship their own dbt changes

• Use AI tools every day, and build the shared skills and workflows the data team relies on

Requirements

• 5+ years in data engineering or analytics engineering, including at least 2 years owning a production data warehouse or dbt project

• Expert SQL and deep experience with a cloud data warehouse (Snowflake preferred), including performance and cost

• You have designed and led a production dbt project: structure, conventions, testing, and CI

• You have built and run ingestion pipelines, with managed tools and with your own code

• You have owned data reliability: monitoring, alerting, incident response, and the fixes that follow

• You have led metric-definition work with senior non-technical stakeholders, such as Finance

• Strong Python for pipelines, automation, and tooling

• You review other people’s code and mentor less experienced colleagues

• You use AI tools (Claude, Cursor, or similar) every day, with specific workflows you can describe and defend

• You want to build data products with AI, not only use AI to write code

• Open-minded: you ask before you assume, and you change your mind when the evidence says so

• You push back on the right questions, and you make design decisions and own them

• Clear, direct communicator, in writing and in conversation

Bonus Points

• Built or owned a semantic layer (Snowflake Semantic Views, dbt Semantic Layer, Cube, LookML, or similar)

• Payments or financial data: Stripe, revenue recognition, reconciliation, month-end close

• Shipped a data product with AI: an AI assistant, text-to-SQL, an LLM feature on data, or evaluation sets for AI answers

• Orchestration and automation tools (n8n, Airflow, Dagster, or similar) and AWS

Benefits

What We Offer

• Hybrid work model (4 days in office, 1 WFH day)

• Unlimited paid time off

• Cibus meal card

• Private health insurance subsidy

• Pension, Keren Hishtalmut & Dmei Havraa

• Real impact at scale — product used daily by thousands of paying customers

• Certified Great Place to Work · Forbes America's Best Startup Employers 2024 & 2025

DoorLoop is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, marital status, pregnancy, genetic information, citizenship status, or any other characteristic protected by applicable law. We use AI-assisted tools, including assessment tools that evaluate cognitive ability and personality fit, to help evaluate applications. Human oversight is maintained and no hiring decision is made solely by an automated system. For positions that can be performed in New York City, candidates may request an alternative selection process. Applicants with disabilities may also request reasonable accommodation at any stage of the hiring process. For any of these requests please contact [email protected]. For more information about the tools we use, bias audit results, and how we collect and protect applicant personal data, please review our Applicant Privacy Notice.