Data Engineer

Staffingine LLC; Arden Hills (MN); On-site; USD 110,000 - 140,000; Part time

Job summary

Staffingine LLC is seeking an experienced Data Engineer for a contract role in Arden Hills, MN. The position involves building modern data architectures—including data warehouses, data lakes, and lake houses—with hands-on Snowflake experience.

The ideal candidate has 7+ years in SQL, data engineering, and data modeling, plus strong Python scripting, CI/CD, and data security practices. You will collaborate with business partners and own end-to-end data solutions.

Qualifications

• 7+ years in SQL, data engineering, and data modeling.
• Snowflake: minimum 2 years hands-on experience.
• Designs and builds ingestion patterns for files, APIs, databases, CDC, replication, and streaming sources.
• Builds, optimizes, and operates ETL/ELT pipelines and integrated datasets.
• Uses CI/CD, automated testing, and deployment practices for data solutions.
• Uses Python for data engineering automation.
• Owns priorities and drives outcomes with minimal supervision.
• Communicates clearly with technical and business partners.
• Applies data security practices: encryption, masking, access-aware design.

Skills

SQL

Data modeling

Data architecture

Python

Problem solving

Communication

CI/CD

Data security

Tools

Snowflake

Azure

Databricks

Power BI

Git

Job description

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.