Senior Data Engineer

Gas South•Gainesville, FL

2d•Hybrid

About The Position

The Senior Data Engineer is responsible for designing, building, testing, and supporting data pipelines, curated datasets, and analytical data models that enable enterprise reporting, self-service analytics, and broader data modernization efforts. This role helps ensure the business has reliable, well-structured, and trusted data to support reporting, analysis, and decision-making. This is a senior individual contributor role focused on hands-on engineering, solution design, and delivery. While this role does not include formal people management responsibilities, it does require strong technical ownership, sound judgment, and the ability to support and mentor less experienced team members. The Senior Data Engineer works closely with business stakeholders, analysts, and other IT teams to understand data needs and deliver solutions that are secure, scalable, and aligned to business priorities.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
5+ years of experience in data engineering, analytics engineering, or enterprise data platform development.
Experience designing, building, and supporting scalable data pipelines and analytical data models.
Strong understanding of data modeling approaches, ETL and ELT patterns, and data storage design for analytics use cases.
Experience working with structured and semi-structured data.
Hands-on experience with Microsoft Azure cloud data services and modern data platform concepts.
Strong SQL skills and experience supporting enterprise reporting, analytics, or business intelligence solutions.
Understanding of data security, governance, and controlled access patterns, and the ability to apply those concepts in solution design.

Nice To Haves

Experience with modern Microsoft data platforms, including Microsoft Fabric and related reporting and integration capabilities.
Experience building or supporting semantic models, curated datasets, or reporting layers for tools such as Power BI.
Familiarity with layered or medallion-style data architectures and common analytical modeling patterns.
Experience contributing to data governance practices, including access controls, data product standards, and self-service guardrails.
Exposure to DataOps or DevOps practices for data platforms, including CI/CD, monitoring, and release management.
Experience working in Agile software development environments.
Experience using Python for data engineering, notebook-based development, orchestration, or automation.
Exposure to Kusto Query Language (KQL) for querying telemetry or event-based datasets.
Strong analytical, troubleshooting, and problem-solving skills.

Responsibilities

Design, build, test, and maintain batch and near-real-time data pipelines that bring together data from a variety of internal and external sources.
Develop and optimize curated datasets, analytical models, and reporting-ready data structures that support enterprise reporting and governed self-service analytics.
Build secure, reliable, and high-performing data solutions using Microsoft Azure and related Microsoft data platform technologies.
Serve as the technical owner for assigned data domains, helping shape design decisions related to ingestion, transformation, modeling, performance, and supportability.
Implement secure and governed data access patterns, including role-based access and other controls that support appropriate self-service use.
Partner with security, compliance, and architecture teams to ensure solutions align with enterprise standards and operational expectations.
Identify opportunities to improve the scalability, resilience, and overall effectiveness of the data platform and supporting processes.
Partner with business stakeholders, analysts, and technical teams to understand requirements and translate them into practical data and reporting solutions.
Develop datasets, metrics, and semantic models that support business intelligence and reporting tools such as Power BI.
Help ensure data products are clearly defined, trusted by users, and aligned to business needs.
Implement data profiling, validation, monitoring, and issue resolution practices that improve the accuracy, timeliness, and reliability of enterprise data.
Participate in operational support for critical data pipelines, including incident triage, root cause analysis, issue remediation, and preventive improvement efforts.
Support DataOps practices such as deployment, monitoring, documentation, and release discipline across data solutions.
Identify and address technical debt to improve the reliability, performance, and scalability of data processes and platforms.
Work collaboratively with data engineers, analysts, architects, and business stakeholders to deliver practical, high-quality data solutions.
Participate in design reviews and code reviews to promote consistency, maintainability, and adherence to engineering standards.
Contribute to proof-of-concept efforts, platform improvements, and automation opportunities that reduce manual effort and improve delivery efficiency.
Document solutions, standards, and best practices, and provide guidance to junior engineers through day-to-day collaboration and mentorship.