Data Engineer

Job Title: Data Engineer Job Location: Arden Hills, MN Job Type: Contract Job Description: Core Experience: 7+ years in SQL, data engineering, and data modeling. Data Platforms: Builds and supports data warehouses, data lakes, and lake house environments. Snowflake: Minimum 2 years of hands-on Snowflake experience. Delivery Leadership: Leads full-lifecycle data engineering or reporting initiatives. Data Ingestion: Designs and builds ingestion patterns for files, APIs, databases, CDC, replication, and streaming/message-based data sources. Data Pipelines: Builds, optimizes, and operates reliable ETL/ELT pipelines and integrated datasets. DevOps: Uses CI/CD, automated testing, and deployment practices for data solutions. Scripting: Uses scripting languages, preferably Python, for data engineering automation. Ownership: Works independently, manages priorities, and drives outcomes with minimal supervision. Problem Solving: Applies strong analytical, troubleshooting, and root cause analysis skills. Communication: Communicates clearly and coordinates effectively with technical and business partners. Data Security: Applies security practices such as encryption, anonymization, masking, and access-aware design. Modern Data Architecture: Understands warehouse, lake, lake house, and cloud-based data architecture patterns. Azure Familiarity: Understands Azure services used for data storage, integration, processing, orchestration, and security. Orchestration: Manages pipeline orchestration, scheduling, monitoring, and data flow reliability. Metadata Management: Uses metadata-driven practices to improve usability, lineage, and governance. Version Control: Uses version control to support quality, traceability, and team collaboration. Scalability: Designs pipelines with scalability, performance, and distributed processing considerations. Competencies-Skills (Preferred): Advanced Platform Optimization: Optimizes complex Snowflake and Databricks/Spark workloads, including streams, tasks, dynamic tables, and performance tuning. HR Data Experience: Works with HR systems such as Workday and supports workforce analytics, employee lifecycle reporting, and people data use cases. HR Data Governance: Applies privacy, minimization, masking, and access practices specifically for confidential employee and workforce data. Enterprise Solution Design: Shapes reusable data product patterns, logical models, and target-state designs for enterprise analytics. Power BI Enablement: Partners with analysts to support semantic models, curated datasets, dashboards, and trusted reporting experiences. Data Vault: Understands Data Vault modeling concepts and architecture. Practical Innovation: Identifies pragmatic opportunities to improve data products, analytics delivery, and user adoption. Agentic AI Exposure: Understands agentic AI concepts and opportunities to apply AI-enabled workflows in data and analytics contexts. AI Productivity: Uses AI tools responsibly to improve personal productivity, streamline analysis, accelerate documentation, and support delivery quality. MLOps Exposure: Understands machine learning operations and production model lifecycle concepts. #J-18808-Ljbffr