Data Engineer Jobs

About The Position

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or another relevant quantitative field.
• Three to five years of experience in data engineering, ETL/ELT development, or a related field.
• Proficiency with SQL and database technologies for data extraction, transformation, and loading.
• Experience with Microsoft Fabric, Azure Data Factory, or similar cloud ETL/orchestration tools.
• Experience with medallion architecture and modern data warehousing patterns.
• Experience with a programming language such as Python, PySpark, or T-SQL for data transformation.
• Familiarity with data modeling techniques, including dimensional modeling and star schemas.
• Understanding of data governance, data quality, and metadata management practices.
• Strong analytical and troubleshooting skills to diagnose and resolve complex pipeline and data-quality issues.
• Excellent time and project management skills, including prioritizing multiple pipeline and infrastructure projects.
• Strong communication skills and ability to explain technical data structures to non-technical stakeholders.
• Strong collaboration skills, particularly with the Data Scientist, Data Analysts, and business-system owners.
• Ability to focus on complex technical problems and work independently with minimal supervision.
• Ability to work in a fast-paced environment and adapt to changing business priorities.
• Meticulous attention to detail and commitment to reliable, well-documented data infrastructure.

Nice To Haves

• Experience preparing data for AI/ML consumption, such as vector embeddings or RAG architectures.
• Business acumen and understanding of construction or related industries.
• Relevant certifications, such as Microsoft Certified: Fabric Data Engineer Associate, Azure Data Engineer Associate, or similar cloud-platform certifications.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines that ingest enterprise-system data into Microsoft Fabric.
• Architect and maintain the Fabric medallion Lakehouse structure, including bronze, silver, and gold layers.
• Develop and implement data infrastructure best practices, including Development/Test/Production environments and Git version control.
• Own pipeline orchestration, scheduling, and monitoring to ensure reliable, timely, and accurate data availability.
• Curate and maintain core datasets across employee, finance, project, service, and manufacturing domains.
• Establish and enforce data quality, validation, and reconciliation processes across pipelines.
• Design and manage data models, schemas, and semantic layers to support reporting and data science work.
• Define and maintain data ontologies and canonical business definitions to ensure consistent meaning across systems and consumers.
• Prepare and structure data for AI and machine-learning use cases, including feature-ready datasets, RAG pipelines, and vector embedding storage.
• Manage Fabric capacity planning, workspace organization, and performance optimization.
• Implement data governance practices, including access controls, lineage tracking, and metadata management, consistent with data classification standards.
• Partner with business-system owners to understand upstream data structures and manage change impacts.
• Collaborate with the Data Scientist to ensure pipeline outputs support analytical and machine-learning use cases.
• Collaborate with Data Analysts to support paginated reporting, dashboards, and self-service BI needs.
• Collaborate with Software Development and DevOps teams to support application development needs.
• Coordinate with third-party consultants as needed to deliver data engineering projects and augment capacity.
• Develop and maintain documentation for pipelines, schemas, and integration logic.
• Troubleshoot and resolve pipeline failures, latency issues, and data-quality incidents.
• Monitor and maintain data-specific infrastructure, including Fabric capacity, orchestration tools, and monitoring and alerting systems.
• Evaluate and recommend data engineering tools, patterns, and best practices.
• Stay current on trends in data engineering, cloud data platforms, and integration techniques.