Data Engineer

eClerx•New York, NY

3h•$120,000 - $130,000

About The Position

eClerx is seeking for an experienced Data Engineer with 5+ years of expertise in data warehousing and ETL development. The ideal candidate will have strong hands-on experience with Snowflake, Talend or Informatica, and Python, along with a proven ability to design and optimize scalable, high-performance data pipelines. This role involves building and maintaining cloud-native data solutions, driving automation and performance improvements, and implementing Agile and DevOps best practices. You will collaborate closely with business, analytics, and IT stakeholders to deliver reliable, cost-efficient data solutions that generate measurable business value. Strong ownership, problem-solving skills, and the ability to thrive in a fast-paced, data-driven environment are essential.

Requirements

5+ years of experience in data engineering, data warehousing, and ETL development.
Advanced proficiency in Snowflake, ETL(Talend Enterprise/ Informatica PowerCenter), and Python.
Strong background in designing and implementing scalable, high-performance data pipelines.
Demonstrated experience in delivering complex projects.
Solid grasp of Agile and DevOps principles with practical experience implementing these in data engineering environments.
Excellent communication and collaboration skills.
Strong sense of ownership, accountability, and proven problem-solving abilities.
Experience driving team transformation and process improvement through automation and cloud adoption.
Ability to foster a culture of innovation, continuous learning, and excellence.
Prior experience working in fast-paced, data-driven organizations.

Responsibilities

ETL and Data Warehousing: Develop, and maintain robust, scalable ETL pipelines and data warehouse solutions using Snowflake, ETL(Talend Enterprise/ Informatica PowerCenter), and Python.
Cloud Data Engineering: Design and implement cloud-native data solutions, ensuring performance, cost-efficiency, and scalability.
Performance Optimization: Continuously monitor and optimize ETL processes and data pipelines for maximum efficiency and reliability.
Agile & DevOps Implementation: Drive the adoption of Agile methodologies and DevOps best practices to enhance team efficiency and accelerate delivery cycles.
Automation & Innovation: Identify opportunities for automation throughout the data lifecycle and implement innovative solutions to streamline operations.
Stakeholder Collaboration: Work closely with business, analytics, and IT stakeholders to understand data requirements and deliver solutions that drive business value.