Data Engineer

SECO Energy is hiring a Data Engineer to build and support enterprise data pipelines, lakehouse and warehouse data products, and governed data platforms. The role develops data transformations and models, implements metadata, quality, governance, and security practices, and partners with business and technical teams to deliver trusted, reusable data products.

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

Preferred

• Certifications or formal training in Microsoft Fabric, Azure Data Engineer, Databricks, Spark, Snowflake, data governance, DevOps, data modeling, or cloud architecture
• 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
• 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
• Azure Data Engineering
• Apache Spark
• Data Modeling
• Cloud Architecture
• Lakehouse Design
• Lakehouse Operations
• Data Warehouse Design
• Data Warehouse Operations
• Medallion Data Flows
• Pipeline Orchestration
• Metadata-Driven Frameworks
• Governed Data Marts
• Semantic-Ready Data Marts
• Delta Lake
• Power BI Semantic Models
• Git-Based DevOps
• Enterprise Operational Data
• Microsoft Fabric Certification
• Azure Data Engineer Certification
• Databricks Certification
• Spark Certification
• Snowflake Certification
• Data Governance Training
• DevOps Training
• Data Modeling Training
• Cloud Architecture Training

Company

• Not-for-profit electric cooperative serving Central Florida members.
• Sumterville, Florida, USA
• https://www.secoenergy.com/
• Founded in 1938
• 501-1000 employees

Qualifications

• 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
• Job-related experience may be substituted for the required education on a year-for-year basis
• 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
• 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
• 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
• Certifications or formal training in Microsoft Fabric, Azure Data Engineer, Databricks, Spark, Snowflake, data governance, DevOps, data modeling, or cloud architecture
• 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
• 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
• Job-related experience may be substituted for the required education on a year-for-year basis.
• 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.

Required skills

• Azure Data Lake Storage, Data Factory, Data Governance and Metadata Management, Databricks, Dimensional Modeling, Git-based DevOps, Microsoft Fabric, PySpark, Python, SQL, Snowflake, Spark SQL

Preferred skills

• AI Tools, Analyze How Well You Fit, Build Cover Letter, CEO, Chief Executive Officer, Company data provided by, Curtis Wynn, Customize Your Resume, Jim Duncan, Late Stage, Leadership Team, Make your application stand out, Maximize your interview chances, Sumterville, Florida, USA, Understand your strength & weakness, https://www.secoenergy.com

Experience

• 4+ years