Job Details

This role is responsible for architecting, developing, and optimizing scalable data pipelines, integrations, and warehouse solutions that support analytics, artificial intelligence (AI), and machine learning initiatives across the organization. The ideal candidate has strong experience in SQL, data modeling, and modern cloud-based data engineering platforms, with a focus on enabling data-driven intelligence at scale.

Key Responsibilities

• Design, build, and maintain robust ETL/ELT pipelines and data workflows using tools such as Microsoft Fabric, Azure Data Factory, or Databricks.

• Develop and optimize SQL-based transformations, ensuring high performance and data reliability across large, complex datasets.

• Engineer and maintain the data warehouse and lakehouse architecture, enabling efficient storage, retrieval, and processing of both structured and unstructured data.

• Prepare and curate high-quality, feature-ready datasets to support AI and machine learning models, ensuring data consistency, accuracy, and scalability.

• Implement and monitor data quality, validation, and governance frameworks to ensure data integrity and compliance.

• Automate data ingestion from multiple systems (ERP, CRM, operational databases, APIs) to create unified, analytics-ready datasets.

• Collaborate with data scientists, analysts, and business stakeholders to ensure that data pipelines and data models align with analytical and AI use cases.

• Identify and execute opportunities for workflow automation, pipeline optimization, and cloud cost efficiency.

• Contribute to data architecture decisions, metadata management, and best practices for version control, CI/CD, and infrastructure-as-code.

Required Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field (or equivalent experience).

• 5+ years of experience in data engineering, ETL development, or data warehousing.

• Advanced proficiency in SQL and experience with relational and dimensional data modeling.

• Hands-on experience with modern data platforms such as Microsoft Fabric, Azure Synapse, or Databricks.

• Strong understanding of ETL/ELT concepts, data pipelines, and data warehouse best practices.

• Familiarity with AI/ML data preparation techniques, such as feature engineering, data normalization, and model input pipeline design.

Preferred Qualifications

• Experience with cloud data ecosystems (Azure, AWS, or GCP).

• Exposure to ML Ops or integrating data pipelines with machine learning platforms.

• Familiarity with infrastructure automation and DevOps (CI/CD pipelines, Git, etc.).

• Experience with Power BI, Tableau, or similar BI tools for semantic modeling and data delivery.

• Experience in the construction or heavy civil industry is a plus.

• Strong communication skills to effectively collaborate across technical and non-technical teams.

Candidates that accept an offer of employment are required to undergo and successfully complete a pre-employment physical examination. This exam is essential to ensure that all potential employees meet the necessary health and fitness standards for the role. The examination includes a series of tests and evaluations conducted by a certified medical professional to assess overall physical health, identify any potential medical conditions that could interfere with job performance, and verify that the candidate is capable of safely performing the essential duties of the position.
Completion and clearance of this physical are mandatory steps in the hiring process.

Equal Opportunity Employer, including disabled and veterans.