Data Platform Engineer

Southeastern University is hiring a Data Platform Engineer to design, build, and maintain its data platform, including pipelines, models, integrations, and analytics-ready datasets. The role turns complex operational data into reliable, secure, well-governed assets and supports institutional reporting and decision-making.

Responsibilities

Design, build, and maintain scalable data pipelines that move data from core university systems into the university’s data warehouse, lakehouse, or analytics environment

Develop and maintain data models using SQL, dbt, and related modern data platform tools

Create reusable, governed, and analytics-ready datasets that support reporting, dashboards, operational workflows, and institutional decision-making

Support the continued development of the university’s data warehouse/data lake architecture

Build data structures that enable self-service analytics, AI-enabled workflows, and faster access to institutional insights

Integrate data from university systems such as the student information system, CRM platforms, LMS, finance systems, advancement systems, housing systems, and other enterprise applications

Transform raw operational data into clean, standardized, and trusted data models

Translate business rules, source system logic, and operational processes into clear data transformations

Partner with application owners and business stakeholders to understand data definitions, system behavior, and reporting needs

Identify and resolve data inconsistencies, duplicate records, missing fields, and process-driven data quality issues

Develop and maintain documentation for data models, pipelines, business definitions, lineage, and transformation logic

Implement data quality checks, validation rules, and testing practices to increase trust in university data

Support institutional data governance practices, including data stewardship, access control, data classification, and consistent definitions

Promote standards for naming conventions, version control, deployment processes, and data modeling practices

Ensure data solutions are secure, FERPA-aware, well-documented, and aligned with university policy

Partner with data analysts, report developers, and university leaders to create datasets that support accurate and timely reporting

Support high-priority institutional areas such as enrollment, advising, financial aid, student clearance, student success, finance, advancement, and academic operations

Help reduce dependency on manual spreadsheets, one-off reporting, and disconnected departmental data processes

Build solutions that are practical, timely, focused, and aligned with the needs of university decision-makers

Assist in creating data products that help departments better understand performance, operations, trends, and opportunities

Monitor and troubleshoot data pipelines, scheduled jobs, integrations, and platform performance

Respond to data issues with urgency, accuracy, and clear communication

Identify opportunities to automate manual processes and improve data delivery

Participate in platform upgrades, migrations, security reviews, and technology modernization efforts

Stay current with emerging trends in data engineering, analytics, AI, cloud platforms, higher education technology, and data governance

Preferred

Master's degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field; equivalent professional experience may be considered

Experience with dbt or similar data transformation frameworks

Experience with cloud data platforms such as Snowflake, BigQuery, Redshift, Azure Synapse, Databricks, or similar technologies

Experience with ETL/ELT tools, APIs, integration platforms, orchestration tools, DLT, or scheduled data workflows

Experience with BI and reporting tools such as Power BI, Looker Studio, Tableau, or similar platforms

Experience working in higher education or with systems such as Jenzabar, Element451, Salesforce, Brightspace/D2L, Slate, Ellucian, Workday, or similar enterprise platforms

Familiarity with data governance, master data management, metadata documentation, and data quality testing

Experience supporting semantic layers, self-service analytics, AI-ready datasets, or institutional data products

Understanding of FERPA and data privacy expectations in a higher education environment

dbt

Data transformation frameworks

Snowflake

BigQuery

Amazon Redshift

Azure Synapse

Databricks

ETL/ELT tools

APIs

Integration platforms

Data orchestration

DLT

Scheduled data workflows

Power BI

Looker Studio

Tableau

Higher education systems

Jenzabar

Salesforce

Brightspace/D2L

Slate

Ellucian

Workday

Data governance

Master data management

Metadata documentation

Data quality testing

Semantic layers

Self-service analytics

AI-ready datasets

Institutional data products

FERPA

Data privacy

Benefits

Main Campus Hybrid (some remote, some main campus, and/or some other physical location)

Company

Christian university offering undergraduate, graduate, and doctoral degrees

Lakeland, Florida, USA

http://seu.edu/

Founded in 1935

501-1000 employees

Qualifications

Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field; equivalent professional experience may be considered

Experience building or supporting data pipelines, data warehouses, data lakes, or analytics platforms

Strong SQL skills and experience working with relational databases

Proficiency in Python for data processing, transformation, automation, or integration tasks

Experience transforming, modeling, and preparing data for reporting, analytics, or operational use

Experience with version control tools such as Git

Strong analytical, troubleshooting, and problem-solving skills

Ability to understand complex business processes and translate them into data requirements

Ability to communicate technical concepts clearly to both technical and non-technical audiences

Strong commitment to data accuracy, documentation, security, and customer service

Master's degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field; equivalent professional experience may be considered

Experience with dbt or similar data transformation frameworks

Experience with cloud data platforms such as Snowflake, BigQuery, Redshift, Azure Synapse, Databricks, or similar technologies

Experience with ETL/ELT tools, APIs, integration platforms, orchestration tools, DLT, or scheduled data workflows

Experience with BI and reporting tools such as Power BI, Looker Studio, Tableau, or similar platforms

Experience working in higher education or with systems such as Jenzabar, Element451, Salesforce, Brightspace/D2L, Slate, Ellucian, Workday, or similar enterprise platforms

Familiarity with data governance, master data management, metadata documentation, and data quality testing

Experience supporting semantic layers, self-service analytics, AI-ready datasets, or institutional data products

Understanding of FERPA and data privacy expectations in a higher education environment

Required skills

Data Pipelines, Data Warehousing, Data Lakes, Analytics Platforms, SQL, Relational Databases, Python, Data Transformation, Data Modeling, Git

Experience

Mid level