Posh aims to solve the problem of declining human connection in the digital age by enabling users to organize in-person events and build communities. The company is scaling rapidly and needs to establish a robust data infrastructure to support its growth and become a data-driven organization.
Requirements
- Has at least four years of hands-on experience in data engineering or analytics engineering. Demonstrates a strong ability to design, build, and optimize scalable data systems.
- Familiar with cloud data platforms (AWS, GCP, Azure), orchestration tools (Airflow, Prefect, Dagster), data integration tools (Fivetran, Estuary), transformation frameworks (dbt), modern data warehouses (BigQuery, Redshift, Snowflake), and NoSQL databases (MongoDB).
- Proficient in SQL-based and NoSQL databases, optimizing query performance, indexing strategies, and ensuring efficient data storage and retrieval.
- Skilled in creating scalable, well-documented data models for self-serve analytics with robust data validation, testing, and observability. Implements proven practices for data governance, integrity, and accessibility to ensure consistent, reliable data throughout the organization.
- Experience creating and cleaning data for production ready ML models and identifying new data sources to increase ML efficacy.
Responsibilities
- 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.
- 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.
- 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).
- 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 protocols, access controls, and compliance standards to safeguard sensitive data and meet regulatory requirements.
- Collaborate with Product and Engineering Teams: Work cross-functionally to define data requirements, design efficient data models, and track product features and metrics. Partner with Engineering teams to implement effective data tracking, logging, and ingestion strategies that align with business objectives.
Other
- This is an in-person position at our New York City office, located in the heart of SoHo.
- Is able to manage multiple projects simultaneously.
- Capable of prioritizing tasks effectively to meet deadlines, ensuring efficient and timely completion of projects.
- Exhibits high interest in startups and has experience building the early foundation of a data team at a small tech company.
