Remote Data Engineer: Pipelines & ML Features on Azure & GCP

Eliassen Group; Boston (MA); Remote; USD 83,000 - 96,000; Full time

Job summary

Eliassen Group is seeking a Data Engineer for a remote, contract role. You will own data pipelines, collaborate across teams, and optimize the data stack on Azure, GCP, and on-premises components. Expect Python-based ML feature engineering, CI/CD, and modern data platforms.

The role emphasizes scalable data workflows, documentation, and cross-functional delivery in a fast-moving retail-domain environment.

Qualifications

• 4+ years of professional data development experience.
• Proficient in SQL and NoSQL concepts and data warehousing basics.
• Hands-on Python development experience and data pipelines.
• Experience with CI/CD pipelines and automated testing.
• Familiarity with cloud data services (GCP, Azure) and big data tools.
• Understanding of Agile/Scrum practices and modern SDLC.

Responsibilities

• Design, build, and maintain scalable data pipelines for ingestion, transformation, and integration using Kafka and Databricks.
• Modernize data assets across SQL, NoSQL, cloud, and real-time streaming platforms.
• Develop feature engineering pipelines for ML using Vertex AI and BigQuery ML.
• Implement automated unit, integration, and performance testing for data quality.
• Optimize data workflows for performance, cost, and scalability across large datasets.
• Draft architecture diagrams and interface specs to communicate data solutions.
• Own systems and drive features to completion across internal/external apps.

Skills

SQL/NoSQL

Python

Data pipelines

CI/CD

Cloud platforms

Spark

Kafka

Databricks

Vertex AI

BigQuery ML

Git/GitHub Actions

NoSQL/SQL data warehousing

Tools

Kafka

Databricks

Vertex AI

BigQuery ML

Python

CI/CD tools

Git

GitHub Actions

Spark

EventHub

Dataflow

Job description

Eliassen Group is seeking a Data Engineer for a remote, contract role. You will own data pipelines, collaborate across teams, and optimize the data stack on Azure, GCP, and on-premises components. Expect Python-based ML feature engineering, CI/CD, and modern data platforms.

The role emphasizes scalable data workflows, documentation, and cross-functional delivery in a fast-moving retail-domain environment.