The Data Engineer is responsible for designing, building, and supporting scalable data platforms, data products, application integrations, and analytics solutions that enable business decision-making across Power Delivery. This role develops and maintains enterprise data pipelines, integrates operational systems with cloud-based lakehouse platforms, and supports both low code and pro-code application development using Microsoft Power Platform, OutSystems, and Databricks. The successful candidate will partner with business stakeholders, data analysts, application developers, and technology teams to deliver trusted, governed, and reusable data assets. Key Responsibilities Data Engineering & Integration Design, build, and support enterprise data pipelines using ETL and ELT methodologies. Develop scalable ingestion frameworks for structured, semi structured, and streaming data. Build and maintain Databricks notebooks, workflows, and data pipelines. Ingest data from source systems using: Azure Data Factory Kafka CDC technologies APIs Files and database sources Implement data quality checks, monitoring, and exception handling. Support Bronze, Silver, and Gold data architectures within the Databricks Lakehouse. Databricks Development Design and maintain Delta Lake tables and data products. Develop solutions using: Databricks PySpark Spark SQL Python SQL Optimize lakehouse performance, scalability, and cost management. Support data sharing, governance, and catalog management using Unity Catalog. Application Development Build and enhance business applications using: Microsoft Power Apps Power Automate Power Platform OutSystems Develop integrations between Databricks and business applications. Partner with users to automate workflows and operational processes. Data Governance & DevOps Follow client governance and security standards. Implement CI/CD practices using GitHub and Azure DevOps. Maintain documentation, lineage, and metadata for enterprise data assets. Participate in architecture reviews and solution design activities. Required Qualifications Education Bachelor's degree in: Computer Science Information Systems Data Analytics Engineering Related technical discipline Experience 3-7 years of experience in data engineering, analytics engineering, or software development. Experience building enterprise data pipelines and integrations. Experience with cloud-based data platforms. Required Technical Skills Data Engineering ETL / ELT design and development Data ingestion frameworks Data pipeline orchestration Data Warehousing concepts Data Lake and Lakehouse architectures Medallion architecture Databricks Databricks Workflows PySpark Spark SQL Delta Lake Unity Catalog Data Products Cloud & Integration Azure Data Factory APIs Kafka CDC technologies SQL Server Development Python SQL GitHub Azure DevOps Business Applications Power Apps Power Automate Power Platform OutSystems Preferred Qualifications Utility industry experience Power Delivery domain knowledge Experience supporting Transmission or Distribution Analytics Real-time data streaming experience Knowledge of AI/ML enablement on Databricks Experience with Power BI integration and Direct Query architectures.
EEO:
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.