Data Engineer

Description

We are looking for a Data Engineer to join a fast-paced IT consulting environment in Atlanta, Georgia. In this role, you will design and optimize modern data solutions that support analytics, machine learning, and business decision-making across a variety of client initiatives. The ideal candidate brings strong technical depth in cloud-based data engineering along with a practical, solution-oriented mindset. This position is well suited for someone who enjoys turning complex data challenges into scalable and reliable platforms.

Responsibilities

Build and maintain robust data pipelines that move, transform, and prepare information for reporting, analytics, and operational use. Design end-to-end data solutions within Azure-based environments, using the right services and frameworks to support performance, reliability, and scale. Develop engineering workflows with Python and PySpark to process both structured datasets and more complex unstructured sources. Create and support data architecture components in Microsoft Fabric, Databricks, and related platforms to enable efficient data access and delivery.

Implement pipeline orchestration and workflow automation through Databricks tools to streamline recurring data operations. Support machine learning initiatives by preparing high-quality datasets and building pipelines that feed model development and deployment processes. Apply sound data management practices to ensure consistency, usability, and governance across data assets. Work within Azure DevOps-driven delivery environments to contribute to version control, deployment processes, and collaborative engineering practices.

Partner with stakeholders to understand business objectives, translate requirements into technical solutions, and deliver strong client-focused outcomes.

Requirements

At least 4 years of experience in data engineering or a closely related field.
Strong understanding of core data management concepts, including data quality, organization, and lifecycle considerations.
Proven experience developing ETL or ELT pipelines in cloud-based environments.
Hands-on expertise with Microsoft Azure, including Azure Databricks and related data platform services.
Proficiency in Python and PySpark for large-scale data processing and transformation.
Experience working with Microsoft Fabric, Databricks workflows, and modern table or storage formats such as Iceberg.
Ability to work effectively with both structured and unstructured data sources.
Strong analytical thinking, problem-solving ability, and a balanced approach to customer service and business priorities.

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