Senior Data Engineer

Posh • New York, NY

13d • Onsite

About The Position

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 position at our New York City office, located in the heart of SoHo.

Requirements

• Possesses 5+ Years of full time Data Experience: 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.
• Strong Experience with Modern Data Stack Technologies: 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).
• Expertise in Database Management and Performance Optimization: Proficient in SQL-based and NoSQL databases, optimizing query performance, indexing strategies, and ensuring efficient data storage and retrieval.
• Expertise in Modeling Best Practices Data Governance, Quality Assurance, and Documentation: 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 building ML Pipelines: Experience creating and cleaning data for production ready ML models and identifying new data sources to increase ML efficacy.
• 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.

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.
• 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 protocols, access controls, and compliance standards to safeguard sensitive data and meet regulatory requirements.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 11-50 employees