Data Engineer

Role: Data Engineer Location: New York (Hybrid) Rate: 55 usd per hour We’re looking for a hands-on Data Engineer to build and scale modern data pipelines in a fast-paced, agile environment. This role is focused on end-to-end data engineering—from designing ETL pipelines to enabling analytics and reporting across the business. Responsibilities • Design, build, and optimize ETL/ELT pipelines using Python and PySpark • Develop and manage Airflow DAGs for orchestration and scheduling • Write efficient, scalable SQL queries across multiple databases (SQL Server, Oracle, etc.) • Build and maintain data models (data warehouse, data lake, MDM concepts) • Integrate systems using REST, SOAP, and ETL frameworks (SSIS or similar) • Develop reusable, production-grade code for data processing workflows • Work with structured and semi-structured data (JSON, large-scale datasets) • Support reporting and analytics via BI tools (Power BI, Tableau, Qlik) • Collaborate within an Agile SDLC team to deliver data solutions end-to-end • Continuously improve data performance, reliability, and scalability Requirements (Must-Haves) • Strong experience in Python for data engineering (non-negotiable) • Hands-on expertise with PySpark / Apache Spark • Experience building pipelines with Apache Airflow (DAGs) • Advanced SQL skills across relational databases (SQL Server, Oracle, DB2, etc.) • Solid understanding of data engineering fundamentals (ETL, data warehousing, data lakes) • Experience with RDBMS concepts and database programming (stored procedures, triggers, DML) • Experience integrating systems via APIs (REST/SOAP) or ETL tools • Strong understanding of data modeling (schemas, ER models)