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Mode of Interview: The State will conduct interviews. The State reserves the right to remove a resource from consideration if the resource is unavailable for interview as requested by the State. Interviews will be conducted via Microsoft Teams. Project Schedule: Anticipated Project Start Date: May 1, 2026 Anticipated End Date: April 30, 2027 The State retains the option to extend the work order in increments determined by the State.
Data Engineer Expert - Remote
Location
United States
Posted
68 days ago
Salary
$100 / month
Seniority
Mid Level
Job Description
Data Engineer Expert - Remote
Novalink Solutions LLC
Position Title: Cloud Data Engineer Summary: The State of Nebraska Department of Health and Human Services (DHHS) is seeking a skilled Cloud Data Engineer to join the Data Office Team in driving the modernization of enterprise analytics. This role will focus on building scalable, high-performance data pipelines and models using modern cloud technologies such as Azure, Databricks, Snowflake, SQL, Python, Scala, and PowerBI. The ideal candidate will bring a strong foundation in data engineering, a passion for solving complex data challenges, and experience working in agile environments. Key Responsibilities: - Design and develop data pipelines and ELT processes to integrate large, diverse datasets from multiple sources. - Build and maintain data models and structures to support enterprise reporting and analytics. - Collaborate with cross-functional teams to deliver BI and analytics solutions that meet business needs. - Optimize data performance by troubleshooting and resolving issues related to large-scale data querying and transformation. - Participate in the design and documentation of data processes, including model development, validation, and implementation. - Contribute to a positive data safety culture by adhering to data governance and security policies. Core Competencies: - Data Structures & Modeling: Design and implement scalable data architectures for structured and unstructured data. - Data Pipelines & ELT: Develop robust extraction, transformation, and loading processes using modern tools and frameworks. - Performance Optimization: Monitor and enhance data performance during development and production. Required Qualifications: - Bachelor’s degree in Data Analytics, MIS, Computer Science, or a related field. - 5+ years of experience in data engineering or data warehouse development, including dimensional modeling. - 5+ years of experience designing and developing ETL/ELT processes using tools like SSIS, Databricks, or Python. - We're looking for a self-motivated team member who can work independently in a fully remote Agile environment - someone who takes ownership of their work, communicates proactively, and drives progress without needing close supervision. - The ideal candidate thrives in a distributed Scrum team, managing their own deliverables and staying accountable through clear, consistent communication.
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• Build tools and frameworks that streamline customer integrations, enabling faster onboarding and better handling of customer data. • Create robust ETLs in PySpark and DBT to process billions of records from customer datasets, ensuring data is accurate, reliable, and ready for downstream use. • Investigate and implement new technologies into the data platform, focusing on practical solutions that address current pain points and anticipate future needs. • Collaborate with product, engineering, and go-to-market teams to design and deliver data solutions for new products and features. • Identify and implement optimizations to improve ETL runtime and data processing scalability, reducing the time and effort required for integrations. • Solve real-world data quality challenges by working directly with messy, incomplete, or inconsistent customer data to extract the signal we need. • Support team members by mentoring engineers, leading technical discussions, and providing clear, actionable feedback.
• Build tools and frameworks that streamline customer integrations, enabling faster onboarding and better handling of customer data. • Create robust ETLs in PySpark and DBT to process billions of records from customer datasets, ensuring data is accurate, reliable, and ready for downstream use. • Investigate and implement new technologies into the data platform, focusing on practical solutions that address current pain points and anticipate future needs. • Collaborate with product, engineering, and go-to-market teams to design and deliver data solutions for new products and features. • Identify and implement optimizations to improve ETL runtime and data processing scalability, reducing the time and effort required for integrations. • Solve real-world data quality challenges by working directly with messy, incomplete, or inconsistent customer data to extract the signal we need. • Support team members by mentoring engineers, leading technical discussions, and providing clear, actionable feedback.
• Design, implement, and maintain robust and scalable data pipelines using AWS, Azure, and containerization technologies. • Develop and maintain ETL/ELT processes to extract, transform, and load data from various sources into data warehouses and data lakes. • Collaborate with data scientists, analysts, and other engineers to ensure seamless data flow and availability across the organization. • Optimize data storage and retrieval performance by utilizing cloud services like AWS Redshift, Azure Synapse, or other relevant technologies. • Work with containerization tools like Docker and Kubernetes to ensure smooth deployment, scalability, and management of data pipelines. • Monitor, troubleshoot, and optimize data processing pipelines for performance, reliability, and cost-efficiency. • Automate manual data processing tasks and improve data quality by implementing data validation and monitoring systems. • Implement and maintain CI/CD pipelines for data workflow automation and deployment. • Ensure compliance with data governance, security, and privacy regulations across all data systems. • Participate in code reviews and ensure the use of best practices and documentation for data engineering solutions. • Stay up-to-date with the latest data engineering trends, cloud services, and technologies to continuously improve system performance and capabilities.

