Design, build, test, deploy, and support governed enterprise data pipelines and lakehouse or warehouse data products for analytics and semantic model consumption. This role focuses on transformation frameworks, CDC-enabled loads, metadata and lineage controls, and data quality practices across modern cloud platforms.
Responsibilities
- 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.
- 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.
- 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.
Requirements
- Education: Four (4) year bachelor’s degree.
- Minimum education field: An accredited institution in computer science, information systems, data engineering, software engineering, mathematics, statistics, engineering, or a related technical field.
- Minimum experience: 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: Certifications or formal training in Microsoft Fabric, Azure Data Engineer, Databricks, Spark, Snowflake, data governance, DevOps, data modeling, or cloud architecture.
- 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.
- Ability to operate a variety of office equipment, including a personal computer, printers, copy machines, telephone.
- Ability to work irregular hours for assignment completion and flexibility to change scheduling and report to work on short notice during emergency situations; irregular hours in evenings and on weekends are possible during peak business times and during storm season.
- Normal work hours are 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.
- Follows safety procedures diligently, identifies potential hazards, and takes appropriate action to maintain a safe working environment.
- Provides exceptional service to members by actively listening to needs and ensuring satisfaction.
- Acts with honesty and integrity in all tasks, maintaining transparency and ethical standards.
- Demonstrates a strong work ethic by meeting deadlines and achieving high performance.
- Contributes to an inclusive culture by respecting and valuing diverse perspectives and collaborating effectively.
- Takes responsibility for actions and decisions to meet commitments and deliver high-quality work.
- Works collaboratively with team members, sharing information and supporting collective goals.
Core and Job-Specific Competencies
- Applies engineering judgment to design reliable, scalable, secure, and maintainable data solutions aligned with organizational architecture and tradeoffs across performance, governance, and usability.
- Builds scalable data pipelines using PySpark, Spark SQL, Python, and SQL for ingestion, transformation, cleansing, enrichment, and validation, optimizing performance and avoiding distributed processing anti-patterns.
- Leverages modern lakehouse and warehouse platforms including Fabric, ADLS, OneLake, Databricks, Snowflake, and Synapse to architect and optimize scalable, secure, and governed data environments.
- Uses Data Factory, Dataflows, notebooks, APIs, and orchestration tools to develop and maintain reliable pipelines supporting scalable processing, monitoring, operational resilience, and production stability.
- Applies dimensional modeling and analytical design with facts, dimensions, keys, slowly changing dimensions (SCDs), bridge tables, conformed dimensions, and aggregate structures for semantic models and BI.
- Establishes governance frameworks covering metadata, lineage, cataloging, auditability, and data quality, implementing validation controls such as reconciliation, integrity checks, and duplication detection.
- Applies MDM principles (entity resolution, survivorship, stewardship) and data-as-a-product thinking to support reusable contracts and governed, consumer-ready outputs.
- Applies Git, CI/CD, code reviews, automated deployments, environment management, and versioning to ensure controlled, repeatable delivery.
- Understands Power BI, Direct Lake, semantic models, gold-layer design, and performance-optimized analytics consumption.
- Uses AI tools (Fabric Copilot and code generation assistants) responsibly, ensuring validation, privacy compliance, and safe production usage.
Technologies
- Microsoft Fabric
- Azure data services
- Azure Data Lake Storage
- OneLake
- Delta Lake
- Databricks
- Snowflake
- Synapse
- PySpark
- Spark SQL
- Python
- SQL
- ETL/ELT
- CI/CD
- Change data capture (CDC)
- Data Factory
- Dataflows
- Notebooks
- APIs
- Git-based DevOps
- Git
- Power BI
- Direct Lake
- Microsoft Purview
- Delta / open table formats
- Dimensional modeling
- Data mesh
- Master data management (MDM)
- DevOps
- DataOps
- Fabric Copilot
Work Location and Experience Level
- Location: Sumterville, FL (onsite)
- Minimum Experience: 4 years
- Education: Four (4) year bachelor’s degree
Verification and Additional Statements
The above qualifications and competencies may be verified through a combination of education, experience, interview questions, and technical skills exercise(s).
This listing’s essential duties and responsibilities are intended to indicate general tasks and levels of work difficulty and do not limit the right of management to assign, direct, and control work. Other duties may be assigned based on the cooperative’s needs.
Physical Demands and Work Environment
- Regularly required to sit and talk or hear.
- 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.
- Must regularly lift and/or move up to 10 pounds.
- Specific vision abilities required: close vision, distance vision, color vision, and ability to adjust focus.
- General sedentary office environment.
- Noise level is usually moderate.
Compliance and Workplace Statements
- SECO Energy participates in E-Verify.
- SECO Energy does not accept unsolicited resumes from any source other than directly from applicants.
- This posting will be closed and no longer available to receive applications upon SECO Energy’s discretion.
Application Context
Irregular hours may occur during peak business times and during storm season. Normal work hours are eight (8) hours between 7:00 am and 5:00 pm, Monday through Friday. Successful completion of a pre-employment background check, physical, and drug screen is required.
