Data Engineer — Remote ETL & Data Pipelines

Digipulsetech; Cherry Hill Township (NJ); Hybrid; USD 120,000 - 160,000; Full time

Job summary

Digipulsetech is seeking a data engineering expert to design, build and maintain scalable data pipelines, datasets and systems for the company’s data infrastructure. You will collaborate with stakeholders to understand data requirements and develop ETL processes to transform raw data into usable formats for analytics and reporting.

You'll implement data models, ensure data integrity and performance, perform quality checks, document pipelines, and share best practices.

Qualifications

• Master’s degree required or Bachelor’s with 5+ years of relevant experience.
• Experience designing and implementing ETL pipelines and data models.
• Strong knowledge of data governance, quality checks and performance optimization.

Responsibilities

• Design, develop and maintain scalable data pipelines and datasets for analytics and reporting.
• Collaborate with stakeholders to gather data requirements and translate them into technical solutions.
• Implement data models to support business analytics and reporting needs.
• Ensure data integrity, consistency and performance across systems.
• Document pipelines, processes and best practices for knowledge sharing.
• Travel to client locations within the United States as needed; relocation/telecommuting may be required.

Education

Master’s degree in computer science/computer applications/engineering

Bachelor’s degree in computer science/computer application(s)/engineering with 5 years progressive experience

Tools

Oracle

SQL Server

UNIX

Python

AWS

Azure

Git

Apache Spark

Kafka

Airflow

Google BigQuery

Informatica

Snowflake

DB2

SQL

ETL

Job description

Digipulsetech is seeking a data engineering expert to design, build and maintain scalable data pipelines, datasets and systems for the company’s data infrastructure. You will collaborate with stakeholders to understand data requirements and develop ETL processes to transform raw data into usable formats for analytics and reporting.

You'll implement data models, ensure data integrity and performance, perform quality checks, document pipelines, and share best practices.