Data Engineer

DATA ENGINEER To Serve the Agricultural Industry MANIFEST Technology is seeking a Data Engineer to build reliable, scalable data solutions that strengthen trusted HR and enterprise analytics. Working closely with business analysts, lead data engineers, and the onsite HR team, this role will design and operate well-managed ingestion patterns, data pipelines, and integrated datasets; improve data quality, governance, and security; and advance data-driven operations across the organization. The ideal candidate brings 7+ years of SQL, data engineering, and data modeling experience, including at least 2 years of hands-on Snowflake experience, and can independently lead full-lifecycle delivery in modern cloud data environments. Duration: 90 days to direct hire Location/Hours: Onsite; Minneapolis Pay Range: Market Rate REQUIRED EXPERIENCE & EDUCATION Core Experience: 7+ years of professional experience in SQL, data engineering, and data modeling. Snowflake: At least 2 years of hands-on experience building and supporting solutions in Snowflake. Modern Data Platforms: Demonstrated experience building and supporting data warehouses, data lakes, and lakehouse environments. Delivery Leadership: Proven ability to lead full-lifecycle data engineering or reporting initiatives from discovery and design through deployment and operational support. QUALIFICATIONS & REQUIREMENTS Data Ingestion: Designs and builds robust ingestion patterns for files, APIs, databases, change data capture, replication, and streaming or message-based sources. Data Pipelines: Builds, optimizes, monitors, and operates reliable ETL/ELT pipelines and integrated datasets. DevOps & Version Control: Applies CI/CD, automated testing, deployment practices, and version control to improve quality, traceability, and team collaboration. Scripting & Automation: Uses scripting languages, preferably Python, to automate data engineering activities and operational workflows. Data Security: Applies encryption, anonymization, masking, and access-aware design to protect sensitive enterprise and HR data. Cloud & Azure Architecture: Understands warehouse, lake, lakehouse, and cloud architecture patterns, including Azure services for storage, integration, processing, orchestration, and security. Orchestration & Reliability: Manages pipeline scheduling, monitoring, dependencies, recovery, and data-flow reliability. Metadata & Governance: Uses metadata-driven practices to improve usability, lineage, discoverability, and governance. Scalability & Performance: Designs pipelines with scalability, performance optimization, and distributed processing considerations. Ownership & Problem Solving: Works independently, manages competing priorities, drives outcomes with minimal supervision, and applies strong troubleshooting and root-cause analysis skills. Collaboration & Communication: Communicates clearly and coordinates effectively with technical and business partners, including analysts, lead engineers, and HR stakeholders. PREFERRED QUALIFICATIONS Advanced Platform Optimization: Experience optimizing complex Snowflake and Databricks/Spark workloads, including streams, tasks, dynamic tables, and performance tuning. HR Data Experience: Experience with HR systems such as Workday and with workforce analytics, employee lifecycle reporting, or people-data use cases. HR Data Governance: Knowledge of privacy, minimization, masking, and access practices for confidential employee and workforce data. Enterprise Solution Design: Ability to shape reusable data-product patterns, logical models, and target-state designs for enterprise analytics. Power BI Enablement: Experience partnering with analysts on semantic models, curated datasets, dashboards, and trusted reporting experiences. Data Vault: Understanding of Data Vault modeling concepts and architecture. Practical Innovation: Identifies pragmatic opportunities to improve data products, analytics delivery, and user adoption. AI & MLOps Exposure: Understanding of agentic AI, responsible use of AI productivity tools, AI-enabled analytics workflows, and production machine-learning lifecycle concepts. #J-18808-Ljbffr