Data Engineer

Data EngineerPosition DetailsSector: Electric Utility, Energy, and PowerLocation: Carmel, IN (Hybrid - 3 days/week onsite)Duration: January 2027 – September 2027Employment Type: Contract (High Likelihood of Extension/Conversion)Position OverviewWe are seeking a Data Engineer to join a growing Data and AI team focused on modernizing how enterprise data is discovered, integrated, governed, and made available for analytics, machine learning, and AI initiatives.This role is primarily focused on data engineering and data pipelines, with opportunities to gain additional exposure to machine learning, AI, and automation. The ideal candidate is a hands-on engineer who is strong in Python, SQL, Azure, and ETL/ELT development and can independently own development tasks while collaborating with senior technical leads, engineers, analysts, and business partners.This is a strong opportunity for a Data Engineer who wants to remain hands-on with core data engineering while working closer to machine learning, GenAI, data automation, and modern data governance.Key ResponsibilitiesDesign, build, and maintain scalable data pipelines and data ingestion processes.Integrate data from multiple enterprise sources into an Azure-based data platform.Develop and support automated data processing and transformation workflows.Improve how enterprise data is discovered, onboarded, governed, and delivered to downstream users.Prepare and structure datasets for analytics, machine learning, and AI use cases.Partner with machine learning, AI, software engineering, and data teams to support new solutions.Improve data quality, reliability, governance, and monitoring across data pipelines.Assist with modernizing legacy data processes and moving toward more automated data ingestion and processing solutions.Troubleshoot data pipeline, integration, and data quality issues.Participate in testing, deployment, and continuous improvement of data solutions.Independently own assigned development tasks while working within the technical direction established by senior engineering leads.Required Qualifications3-5 years of professional experience in Data Engineering or related software/data development roles.Strong programming experience with Python.Strong proficiency with SQL.Hands-on experience designing and developing ETL/ELT pipelines.Experience working with Azure data technologies and cloud-based data environments.Solid understanding of data modeling, transformation, integration, and ingestion.Experience integrating APIs and working with structured and semi-structured data from multiple sources.Working knowledge of CI/CD and modern software development practices.Ability to troubleshoot data pipeline, integration, and data quality issues.Strong communication and collaboration skills with the ability to work across technical and business teams.Preferred QualificationsExposure to machine learning pipelines or MLOps.Experience preparing, transforming, and managing datasets used by machine learning models.Familiarity with AI and Generative AI applications and how enterprise data is used to support AI solutions.Experience with Microsoft Purview or similar data governance and cataloging technologies.Experience automating data ingestion and data onboarding processes.Familiarity with enterprise data architecture and cloud-based data platforms.Technical FocusPrimary:Data Engineering | Azure | Python | SQL | ETL/ELT | Data PipelinesSecondary:Machine Learning | Data Automation | Data GovernanceAdditional Exposure:AI/GenAI | MLOps | Microsoft PurviewIdeal Candidate ProfileThe ideal candidate is first and foremost a Data Engineer. They should be comfortable building pipelines and working hands-on with Python, SQL, and Azure, while having enough exposure to machine learning to understand how well-structured enterprise data supports ML and AI solutions.This person does not need to be a senior AI/ML engineer. A strong fit would be someone who has built a solid foundation in data engineering and is interested in expanding their exposure to machine learning, AI, automation, and modern data governance.They should also be capable of independently owning development work while collaborating with and taking architectural direction from senior technical leads.