Posh is looking to establish and scale its data infrastructure and data engineering department to support its growth as a data-driven organization. The company aims to overcome the challenges of digital isolation by fostering real-world human connections through its event organizing platform and needs robust data systems to enable efficient analytics, decision-making, and product development.
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), IaC environments (Terraform), 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
- 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.
- 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).
- Implement and enforce data governance best practices to maintain data integrity, accuracy, and accessibility.
- Create clear documentation and establish company-wide data policies.
- 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.
- Establish and enforce data collection standards to ensure consistency, reliability, and scalability.
Other
- This is an in-person position at our New York City office, located in the heart of SoHo.
- Highly Organized, Proactive, and Efficient:
- Is able to manage multiple projects simultaneously.
- Capable of prioritizing tasks effectively to meet deadlines, ensuring efficient and timely completion of projects.
- Has a Background in Early Stage Data Team (Required): Exhibits high interest in startups and has experience building the early foundation of a data team at a small tech company.
