Senior Data Engineer
PoshNew York CityPosted 27 March 2026
Job Description
Senior Data Engineer
ABOUT POSH
We are all social creatures, but the dominant “social” companies today have evolved into digital loneliness machines, driving isolation, anxiety, and mental health challenges around the world.
Human connection is lost. Posh is a beacon guiding us back.
Posh enables anyone to build an IRL community based on shared interests, while connecting consumers with the communities of people just like them. Founded by event organizers who were frustrated with the growing loneliness epidemic and the tools available to build their own event brand, we’ve built the ultimate platform for launching, monetizing, and finding IRL communities of people just like you. In just 5 years, Posh has grown to a team of 65, expanded to 7M+ users, secured $70m in venture funding, and facilitated over $300M in transactions.
ABOUT THE ROLE
We are looking for an experienced Senior Data Engineer to lay the foundation for our data infrastructure and drive the evolution of our data systems as we scale our Data Engineering department. As our founding Data Engineer, you’ll have the rare opportunity to architect and build scalable data solutions from the ground up, define best practices, and create a robust, high-performing data environment that fuels our company’s growth as a data-driven organization.
At Posh, you'll design and manage our data warehouse, infrastructure, and pipelines. You'll integrate new data sources and optimize transformations to enable efficient, reliable analytics. Working across the entire data stack—from ingestion to transformation to storage and performance tuning—you'll build robust, scalable, and efficient data systems.
You'll collaborate closely with Product and Engineering teams to define data models, establish governance practices, and create infrastructure that drives company-wide decision-making. Your responsibilities will include maintaining data security, reliability, and monitoring to ensure a trusted, scalable environment.
This role offers a high-growth opportunity as we expand our data capabilities and team. If you're passionate about building from scratch, driving best practices, and making a lasting impact, this is the role for you.
This is an in-person role based in our New York City office in the heart of SoHo. Building live experiences and building with soul are important for our customers, and we believe being and working together as a team is the best way for Posh to feel and achieve this. Events on Posh should be fun, alive, and driven with passion and we want to mirror this in our teams every day!
AT A HIGH LEVEL, YOU’LL BE IN CHARGE OF:
- Designing, Building, and Maintaining Scalable Data Pipelines: Design and optimize robust ETL/ELT pipelines that efficiently process and integrate data from multiple sources while ensuring scalability and reliability using Python and SQL.
- Optimizing Data Infrastructure for Performance and Scalability: Enhance data architecture, storage solutions, and processing frameworks to handle growing data volumes while reducing latency and maximizing cost efficiency, to support real-time and batch data processing).
- Ensuring Robust Data Governance and Management: Implement and enforce data governance best practices to maintain data integrity, accuracy, and accessibility. Create clear documentation and establish company-wide data policies.
- Collaborate with Product/Engineering Teams to Build Dashboards: Work cross-functionally to define data requirements, design efficient data models, and track product features and metrics via dashboards. Partner with Engineering teams to implement effective data tracking, logging, and ingestion strategies that align with business objectives.
- Drive Best Practices for Data Collection: Establish and enforce data collection standards to ensure consistency, reliability, and scalability. Set up automated monitoring, logging, and alerting for data pipelines to ensure reliability efficient QAing. Maintain security pro ... (truncated, view full listing at source)
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