Description

General Purpose of Job

Data Engineer role is responsible for design, build, test, deployment, and support of enterprise data pipelines (CI/CD), lakehouse tables, transformation frameworks, change data capture (CDC), metadata controls, data quality processes, and governed data products. This role is responsible for implementing reliable data movement and transformation patterns across modern cloud data platforms, including Microsoft Fabric, Azure data services, Databricks, Snowflake, or comparable lakehouse and warehouse technologies. The Data Engineer should be able to transform data using PySpark, Spark SQL, Python, SQL, and deep understanding of related frameworks, and deep understanding of related frameworks The role requires practical understanding of medallion architecture, Delta / open table formats, data catalogs, data products, dimensional modeling, metadata management, master data management, governance, security, data mesh, domain-driven design, DevOps, observability, and operational support. The position supports the full lifecycle of data products: ingestion, raw/bronze landing, source registration, technical staging, silver conformance, gold semantic publishing, metadata capture, quality validation, promotion, monitoring, and support. The engineer partners with analysts, architects, business SMEs, data stewards, security teams, and platform administrators to deliver data products that are trusted, documented, secure, reusable, and performant.

Minimum Required Qualifications and Competencies

The following includes the minimum job requirements and essential duties for this position. Reasonable accommodation may be made to enable qualified individuals with disabilities to perform the essential functions. Some job requirements may exclude individuals that cannot be reasonably accommodated or who pose a direct threat or significant risk to the health and safety of themselves or other employees.

Requirements

Education
• Minimum: Four (4) year bachelor's degree from an accredited institution in computer science, information systems, data engineering, software engineering, mathematics, statistics, engineering, or a related technical field.
• Preferred: Certifications or formal training in Microsoft Fabric, Azure Data Engineer, Databricks, Spark, Snowflake, data governance, DevOps, data modeling, or cloud architecture.
• Job-related experience may be substituted for the required education on a year-for-year basis.

Experience
• Minimum: Four (4) years of progressively responsible experience in data engineering, ETL/ELT development, analytics engineering, database development, BI engineering, software development, cloud data platform engineering, or related technical work.
• Preferred: Experience designing and operating lakehouse or warehouse platforms, medallion data flows, Spark/PySpark workloads, Data Factory or pipeline orchestration, metadata-driven frameworks, dimensional models, master data, and governed semantic-ready marts.
• Preferred: Experience with Microsoft Fabric, Azure Data Lake Storage, OneLake, Delta Lake, Synapse, Databricks, Snowflake, Power BI semantic models, Git-based DevOps, and enterprise operational data.

Other Requirements
• Ability to operate a variety of office equipment, including a personal computer, printers, copy machines, telephone.
• Ability to work irregular hours in evenings and on weekends for assignment completion and flexibility to change scheduling and report to work on short notice during emergency situations.
• Normal work hours shall be eight (8) hours between 7:00 am and 5:00 pm, Monday through Friday.
• Successful completion of pre-employment background check, physical and drug screen.

Personal Protective Equipment - No

Living Requirement - No

Driving Requirements - No

Core Competencies
• Safety: Follows safety procedures diligently, identifies potential hazards, and takes appropriate action to maintain a safe working environment.
• Member Commitment: Provides exceptional service to members, actively listening to their needs and ensuring their satisfaction.
• Honesty & Integrity: Acts with honesty and integrity in all tasks, maintaining transparency and ethical standards.
• Work Ethic: Demonstrates a strong work ethic by consistently meeting deadlines and achieving high performance in all tasks.
• Inclusive Culture: Contributes to an inclusive culture by respecting and valuing diverse perspectives and collaborating effectively with all team members.
• Accountability: Takes responsibility for their actions and decisions, ensuring they meet commitments and deliver high-quality work.
• Teamwork: Works collaboratively with team members, sharing information and supporting collective goals.

Job Specific Competencies
• Data Engineering Judgment & Solution Design: Applies engineering judgment to design reliable, scalable, secure, and maintainable data solutions aligned with organizational architecture and tradeoffs across performance, governance, and usability.
• Distributed Data Processing, Transformation & Optimization: Applies PySpark, Spark SQL, Python, and SQL to build scalable data pipelines for ingestion, transformation, cleansing, enrichment, and validation, while optimizing performance and avoiding distributed processing anti-patterns.
• Data Platform & Lakehouse Architecture: Leverages expertise in modern lakehouse and warehouse platforms, including Fabric, ADLS, OneLake, Databricks, Snowflake, and Synapse, to architect and optimize scalable, secure, and governed data environments using industry-standard storage and data management practices.
• Data Pipeline Engineering & Operational Reliability: Leverages expertise in Data Factory, Dataflows, notebooks, APIs, and orchestration tools to develop and maintain reliable data pipelines that support scalable processing, operational resilience, monitoring, and production stability.
• Dimensional Modeling & Analytical Design: Demonstrates expertise in dimensional modeling and analytical design, utilizing facts, dimensions, keys, slowly changing dimensions (SCDs), bridge tables, conformed dimensions, and aggregate structures to support semantic models and business intelligence solutions.
• Data Governance, Metadata & Quality Management: Establishes governance frameworks covering metadata, lineage, cataloging, auditability, and data quality. Implements validation controls including reconciliation, integrity checks, duplication detection, and compliance with security standards.
• Master Data, Domain Ownership & Data Products: Applies MDM principles (entity resolution, survivorship, stewardship) and domain-driven design with data-as-a-product thinking, reusable contracts, and governed, consumer-ready data products.
• DevOps & DataOps Practices: Applies Git, CI/CD, code reviews, automated deployments, environment management, and versioning to ensure controlled, repeatable, and reliable data delivery.
• Business Intelligence & Semantic Modeling: Exhibits understanding of Power BI, Direct Lake, semantic models, gold-layer design, and performance-optimized analytics consumption.
• AI-Assisted Engineering Practices: Applies AI tools (Fabric Copilot, code generation assistants) responsibly, ensuring validation, privacy compliance, and safe production usage.

Verification
The above qualifications and competencies for this position may be verified through a combination of education, experience, interview questions and technical skills exercise(s).

Essential Duties and Responsibilities
This description is intended to indicate the kinds of tasks and levels of work difficulty required of the position given this title and shall not be construed as declaring what the specific duties and responsibilities of any position shall be. It is not intended to limit or in any way modify the right of management to assign, direct and control the work of employees under supervision. The listing of essential duties and responsibilities shall not be held to exclude other duties that may be assigned based on the needs of the cooperative.

Platform and Pipeline Engineering
• Design, build, test, deploy, and support modern data platform pipelines and lakehouse or warehouse data products.
• Implement ingestion, staging, transformation, conformance, and publishing patterns across raw/bronze, stage, silver, gold, and certified consumption layers.
• Build PySpark, Spark SQL, Python, and SQL transformations that are distributed, restartable, idempotent, and operationally supportable.
• Implement full-refresh, incremental, CDC, merge, and partition-aware load patterns where appropriate.
• Design source registration, audit stamping, row hashing, metadata capture, and lineage-friendly table handling.

Data Modeling, Metadata, and Master Data
• Build conformed dimensions, facts, bridge tables, reference tables, and curated marts for analytics and semantic model consumption.
• Partner with business SMEs and analysts to define business keys, surrogate keys, grain, relationships, slowly changing dimension needs, and metric-ready structures.
• Implement metadata-driven configuration patterns for sources, tables, columns, jobs, schemas, quality rules, and load audits.
• Support MDM-ready outputs, reference-data management, and stewardship workflows for core entities such as customer, account, product, location, device, meter, vendor, or chart of accounts.
• Maintain clear documentation of table contracts, lineage, refresh behavior, data quality rules, and operational dependencies.

Governance, Security, and Data Product Management
• Apply governance and security practices across data products, including access control, sensitivity awareness, ownership, cataloging, endorsement, certification, and audit evidence.
• Support Microsoft Purview, OneLake catalog, or equivalent governance capabilities for lineage, data cataloging, domains, glossary terms, quality status, and data product discoverability.
• Design solutions that support domain-oriented ownership, data mesh principles, reusable platform patterns, and federated governance.
• Ensure production reports and semantic models consume approved data products rather than unfinished engineering layers unless explicitly approved for validation.

Physical Demands and Work Environment
The physical demands and work environment described here are representative of those that must be met by or those an employee encounters to successfully perform the essential functions of this position. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Some requirements may exclude individuals that cannot be reasonably accommodated or who pose a direct threat or significant risk to the health and safety of themselves or other employees.

While performing the duties of this job, the employee is regularly required to sit and talk or hear. The employee is occasionally required to walk; stand; use hands to finger, handle, or feel; reach with hands and arms; climb or balance; stoop, kneel, crouch, or crawl. The employee must regularly lift and/or move up to 10 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, and the ability to adjust focus. This position has general sedentary office environment. The noise level in the work environment is usually moderate.