Sr Data Engineer
Key details
- Location
- Atlanta, GA, United States
- Work style
- Hybrid
- Level
- Senior
- Job type
- Full-time
- Experience
- 8–11 yrs
- Function
- Data & ML
- Focus area
- Data engineering & Analytics
- Posted
- 52m ago
Skills
About the role
About NISC
NISC develops and implements enterprise-level and customer-facing software solutions for over 1200+ energy cooperatives and communication organizations across North America. Our mission is to deliver technology solutions and services that are Member-focused, quality-driven and valued-priced. We exist to serve our Members and help them serve their communities through our innovative software products, services and outstanding customer support. We are an AI-forward company committed to being a technology leader in our industry.
Position Overview:
We are seeking an experienced Senior Data Engineer to join our growing team of data experts. This position will be responsible for designing, building, and optimizing enterprise data solutions, including data pipelines, semantic data models, and AI-ready data architectures across our cloud-based data platform. The Senior Data Engineer will support our application experts, software developers, database architects, and data analysts on a Data Roadmap strategy and will ensure optimal data delivery architecture is consistent throughout ongoing projects.
This individual will play a key role in making enterprise data more discoverable, trustworthy, governed, and accessible for analytics, reporting, intelligent applications, and emerging AI solutions. They must be comfortable supporting the data needs of multiple teams, systems, and products. We look for individuals who will thrive in a team environment, be committed to accomplishing a common goal, and be excited by the prospect of building scalable data solutions that empower business insights and innovation while having some fun along the way.
Work Schedule:
- Hybrid from one of our office locations:
- Atlanta, GA
- Cedar Rapids, IA
- Lake Saint Louis, MO
- Mandan, ND
- Hybrid Schedule: Minimum of working 3 day per week out of an office location and ability to work up to all 5 days a week from an office location.
- Required Days from an Office Location: Tuesday and Wednesday - the third required day will be up to the candidate and their supervisor to choose.
Essential Duties and Responsibilities:
- Design, develop, and maintain scalable data pipelines and integrations that support enterprise analytics and business operations.
- Build and optimize data solutions across modern cloud-based data platforms and lakehouse environments.
- Design and implement semantic data models and business-ready data layers that improve data accessibility, consistency, and usability.
- Support AI, machine learning, and advanced analytics initiatives through the creation of trusted, well-governed, and scalable data assets.
- Partner with software developers, data analysts, product teams, and business stakeholders to understand data requirements and deliver effective solutions.
- Monitor, troubleshoot, and improve data quality, reliability, and performance across the data ecosystem.
- Contribute to data modeling, metadata management, and data governance practices that improve enterprise data understanding and reuse.
- Research and recommend emerging technologies and best practices related to data engineering, analytics, and AI enablement.
- Create and maintain a culture of engagement and one that is conducive to NISC's Statement of Shared Values.
- Commitment to NISC's Statement of Shared Values.
- Other duties as assigned.
Knowledge, Skills & Abilities Preferred:
- 8 years of experience in a Data Engineering, Software Engineering or related role.
- Experience building and optimizing data pipelines, data integrations, and large-scale data solutions.
- Strong understanding of data warehousing, lakehouse architectures, and data modeling concepts.
- Experience designing semantic data models, business data layers, or related solutions that support reporting, analytics, and AI-driven applications.
- Familiarity with metadata management, ontology concepts, knowledge graphs, or similar approaches used to improve data discoverability and business understanding.
- Experience supporting AI, machine learning, generative AI, or intelligent automation initiatives through scalable data architectures.
- Strong SQL and data analysis skills with experience working across structured and unstructured datasets.
- Experience working with cloud-based data platforms such as Databricks, Snowflake, Microsoft Fabric, AWS analytics services, or similar technologies.
- Experience with AWS services, Spark, ETL/ELT processes, and modern data integration tools.
- Experience supporting and collaborating with cross-functional teams in a fast-paced environment.
- Strong verbal and written communication skills.
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