Data Engineer (SQL, ETL, Snowflake)

Role: Data Engineer (SQL, ETL, Snowflake)
Location: Arden Hills, MN (Onsite )
Duration:  Contract

Experience : 10+ Years

Job Summary: The Data Engineer will build reliable, scalable data solutions that strengthen trusted HR and enterprise analytics. This role works closely with business analysts, lead data engineers, and the HR team on site to deliver well-managed data pipelines, improve data quality, and improve data-driven operations across the organization.

Competencies-Skills (Required):

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.