Data Engineer

Job Summary This role will help maintain and improve data pipelines, Lakehouse tables, and quality checks that feed HR dashboards and reporting. The Data Engineer I will work with the People Analytics team, HRIS, and IT to ensure that HR data is accurate, reliable, and ready for analysis. This is a hands‑on learning role ideal for someone early in their data engineering or analytics career who wants experience with cloud data platforms, HR workflows, and enterprise‑scale reporting. Key Responsibilities Data Engineering & Pipeline Support (50%) Assist in maintaining automated data ingestion from HR source systems (e.g., Workday RaaS, Qualtrics, ServiceNow) using REST APIs and scheduled notebooks. Help build and troubleshoot ETL/ELT processes using Python, PySpark/SparkR, SQL, and Microsoft Fabric notebooks. Support the creation and maintenance of Delta Lake/Lakehouse tables, including basic schema updates and monthly snapshots. Monitor pipeline executions and assist with logging, error handling, and basic performance improvements. Contribute to data quality checks to ensure timely and accurate HR datasets. Data Modeling & Data Preparation (25%) Help transform raw HR data into structured, analysis‑ready datasets for headcount, turnover, recruitment, learning, compensation, and other domains. Support development of semantic layers, shared dimensions, and metric definitions used by Power BI dashboards. Follow established data governance practices, including naming conventions, documentation, and metadata capture. Analytics Enablement (15%) Assist analysts by preparing clean datasets, writing optimized queries, and supporting reproducible analytics workflows. Help maintain trended metrics, time‑series tables, and other recurring data structures needed for enterprise reporting. Ensure that models are structured to support Power BI dashboard needs and self‑service analytics. Collaboration & Learning (10%) Work with People Analytics team, HRIS, and IT to support Dallas College’s People Analytics roadmap. Participate in data governance efforts, security reviews, and documentation updates. Contribute to knowledge transfer by writing clear notes, process guides, and pipeline documentation. Engage in professional development to learn best practices in data engineering, cloud analytics, and HR data structures. This position requires regular on‑site presence to fulfill essential duties that cannot be performed remotely. Duties must be executed in‑person to ensure effective collaboration, supervision, and service delivery, in accordance with Texas SB 2615. If physical presence is not essential, this position may be eligible for remote work only under conditions permitted by Texas SB 2615, including temporary illness, disability accommodations, or assignment to approved remote instruction programs. Required Dallas College Professional Development Hours per Academic Year: all employees are required to complete a minimum of 19 hours. Required Qualifications Technical Knowledge Foundational understanding of data pipelines, ETL/ELT, and data modeling. Ability to write Python/R scripts and SQL queries. Willingness to learn Delta Lake, distributed computing, and cloud analytics architecture. Exposure to API concepts and structured data formats (JSON, CSV, XML). Ability to quickly learn HR business logic and translate it into structured data. Analytical and Architectural Skills Document logic, follow modeling patterns, and understand existing data flows. Build clean, reproducible datasets with clear lineage. Consider upstream/downstream impacts when modifying logic. Operational and Engineering Skills Test and validate data pipelines and identify root causes of errors. Work with version control, CI/CD concepts, and notebook‑based workflows. Produce maintainable, well‑documented code. Collaboration and Communication Clear communication with partners who have varying levels of technical expertise. Translate technical steps into meaningful explanations. Meet deadlines and coordinate with HR, IT, and People Analytics teams. Professional Attributes High attention to detail and a strong sense of responsibility for data quality. Desire to grow technical skills and take on progressively complex engineering work. Adaptability in a fast‑evolving analytics environment with new tools and technologies. Minimum Requirements 0–2 years of experience in data engineering, data analytics, or related technical work (internship or project experience acceptable). Foundational knowledge of Python or R and SQL. Familiarity with cloud data tools (e.g., Microsoft Fabric, Databricks, Snowflake, Azure, or similar) preferred. Understanding of basic ETL/ELT concepts, data cleaning, and data transformation. Ability to learn REST APIs, JSON parsing, and automated ingestion patterns. Basic understanding of data modeling concepts such as tables, joins, relational structure, and time‑series data. Familiarity with Git/GitHub or willingness to learn. Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Statistics, or related field. Equivalent experience or technical certifications (e.g., Azure, Databricks, Python) also considered. Preferred Qualifications (Not Mandatory) Exposure to HRIS or enterprise systems (e.g., Workday, PeopleSoft, SAP). Experience with Power BI, semantic models, or dashboard development. Coursework or project experience with Delta Lake or distributed computing. Some knowledge of DAX, R, PySpark/SparkR, or statistical modeling. Exposure to data governance concepts (data quality, metadata, lineage). *** Will be subject to a criminal background check. Some positions may be subject to a fingerprint check. *** In accordance with applicable law, Dallas College will make reasonable accommodations for applicants and employees’ religious practices and beliefs, as well as any mental health or physical disability needs. #J-18808-Ljbffr