Data Engineer II

Florida Gulf Coast University•Lexington, MA

About The Position

The Data Engineer II supports data informed decision making by designing, building, and maintaining modern data pipelines, data models, and enterprise data warehousing solutions aligned with institutional goals. The role ensures reliable, scalable data ingestion, transformation, and delivery while maintaining high standards for data quality, integrity, and governance. It collaborates closely with functional and technical stakeholders to translate institutional and analytical requirements into sustainable data engineering solutions. FGCU is building a culture of curiosity, commitment and collaboration. We value employees who successfully work with others and drive positive change through critical thinking and decisive action. If you thrive in an environment of innovation, accountability and mutual respect, you will find a good home here.

Requirements

Eight years of directly related full-time experience or a Bachelor’s degree from an accredited institution in an appropriate area of specialization and four years of full-time experience directly related to the job functions.
Experience working with containerized development environments.
Experience using SQL and Python.
Experience supporting system-to-system data integration or data movement using relevant tools or technologies.
Experience working with data systems that support reporting, analysis, and business decision-making.
Knowledge of data engineering, relational databases, SQL, data modeling, and cloud data warehousing concepts supporting analytics and reporting.
Knowledge of data integration methods using APIs, enterprise integration tools (e.g., Boomi), and containerized environments (e.g., Docker) supporting scalable data workflows.
Knowledge of data governance, data quality, and institutional reporting concepts, including enterprise systems (e.g., Banner, Workday) and BI tools (e.g., Tableau, Power BI).
Skill in designing and maintaining data pipelines, data models, and analytical workflows using SQL, Python, APIs, and related tools.
Skill in transforming, validating, and optimizing data within cloud data warehouse environments (e.g., Snowflake).
Skill in developing dashboards, reports, and visual analytics using BI tools such as Tableau and Power BI.
Skill in organizing and coordinating data engineering work, documenting processes, and managing multiple technical priorities.
Skill in analyzing complex datasets, applying analytical reasoning, and communicating insights and recommendations clearly to both technical and non-technical audiences.
Ability to translate business needs into technical data and reporting solutions.
Proven ability to think strategically and approach challenges with creativity.
Demonstrated track record of reliability, meeting goals, and holding oneself accountable.
Strong interpersonal skills and experience working effectively across teams.

Nice To Haves

Master’s Degree from an accredited institution in Computer Science, Data Science, Data Engineering, Information Systems, or related field.
Experience in systems analysis, process design, and end‑to‑end solution development in complex organizational environments, translating business needs into scalable technical solutions.
Experience consuming and integrating data from RESTful APIs and external systems to support automated data ingestion and system interoperability.
Work experience in higher education.
Experience working with enterprise administrative systems such as Banner, Workday, or similar Student Information Systems or ERP platforms, including reporting, data integration, and database development.
Strong experience using SQL and Python for data transformation, automation, validation, and development of scalable analytical workflows.
Experience with Snowflake, including data ingestion, modeling techniques, performance tuning, cost optimization, and security configuration.
Experience with the administration, architectural design, development, and support of reporting and analytics platforms used for dashboards, visual analytics, and self-service reporting.
Experience designing, building, and maintaining data pipelines using Python, Docker, RESTful APIs, and Boomi.

Responsibilities

Partners with stakeholders to determine data requirements and desired outcomes, translating strategic and operational needs into sustainable data engineering and analytics solutions.
Designs, builds, and maintains modern, automated data pipelines and system‑to‑system integrations to support reliable ingestion, transformation, and delivery of data using containerized services, scripting languages, APIs, and enterprise integration platforms such as Boomi.
Develops, maintains, and optimizes enterprise data warehousing solutions within cloud‑based analytical platforms such as Snowflake, utilizing modern data modeling methodologies including Data Vault 2.0 and SQL‑based transformation and querying to support scalable, auditable analytics.
Designs, develops, and maintains analytical and reporting data models that support complex statistical analysis, operational reporting, and institutional decision‑making.
Collects, manages, validates, and cleans data from multiple internal and external sources; performs root‑cause analysis of data issues and develops documented standards, procedures, and controls to ensure ongoing data quality, integrity, and governance.
Analyzes data using quantitative and qualitative techniques to validate pipeline outputs, evaluate results, and support continuous improvement of analytics and reporting processes.
Designs, builds, and supports visual analytics, dashboards, and reporting solutions using business intelligence platforms such as Tableau and Power BI, enabled by well‑modeled and curated datasets.
Creates, maintains, and updates complex reports, accreditation documentation, and assessment materials by ensuring the underlying data structures, warehouse models, and transformations are accurate, complete, and reliable.
Utilizes appropriate statistical and analytical software to analyze and interpret data in support of reporting, assessment, and institutional research needs.
Reviews data engineering and analytics processes, evaluates outcomes, recommends improvements, and promotes best practices in data integration, data warehousing, analytics, and reporting.
Researches, develops, and implements new techniques, tools, and procedures that enhance the university’s data infrastructure, analytics capabilities, and reporting efficiency.
Collaborates with other institutional departments to create and enforce data standards, definitions, coding and reporting practices.
Develops new techniques and procedures to enhance university analytics process.
Performs other duties as assigned.