Top Real Estate Data Engineering Jobs in New York City, NY

Lead development and ownership of BigQuery data assets, pipelines, and observability. Build and optimize dbt models, CDC/file/streaming ingestion, and streaming replication. Partner with engineering and business teams to enable self-service analytics, advanced analytics (LTV, churn, attribution), and reliable real-time data products.

The GTM Engineer will innovate and enhance GTM processes at EliseAI by leveraging AI and automating workflows while collaborating across various teams.

As a Staff Analytics Engineer, you'll lead analytics architecture, ensure data quality, collaborate with stakeholders, and drive AI-powered data insights for business decision-making.

The Data Engineer will build data transformation systems and connect diverse data sources to create transparent pricing for small businesses. Emphasis on accuracy and customer satisfaction is key.

The Head of Data Engineering will lead and build the data engineering function, modernize data infrastructure, establish governance, and optimize data workflows while collaborating with cross-functional teams.

Lead and implement data and analytics solutions, architect data systems, provide assessments and roadmaps, and mentor teams in a consulting environment.

Own end-to-end data and analytics for SaaS clients: collect requirements, stand up data infrastructure, migrate reporting to Valon Data Schema, build dbt pipelines, rewrite SQL queries and dashboards, conduct analyses, and support client operations under tight deadlines.

The Data Engineer designs and builds AI-driven solutions, optimizes data pipelines, manages integrations, and enhances analytics capabilities across the organization to improve efficiency and decision-making.

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

Lead the Engagement Data Warehouse team, owning the vision for data products and managing a team of data engineers. Drive data roadmap, engineering excellence, and foster team culture within data-intensive systems.