Top Entry Level Data Engineer Jobs in Dallas, TX

Designs and maintains scalable ETL/ELT pipelines and cloud data solutions using Azure Data Factory, Databricks, SQL, Python, and Java. Supports Databricks environments, Azure infrastructure, data quality, testing, CI/CD, monitoring, troubleshooting, and performance optimization. Integrates data for AI, machine learning, business intelligence, automation, and advanced analytics while collaborating with technical and business stakeholders.

Designs, deploys, and optimizes production-grade data infrastructure, ETL/ELT pipelines, distributed databases, streaming systems, vector databases, APIs, and feature stores for AI and machine learning applications. The role also implements data quality, observability, lineage, testing, compliance, and secure cloud-native deployments. As a forward-deployed engineer, the employee partners directly with customers, prototypes solutions, transitions them to production, and feeds field learnings into platform improvements.

Participates in a 12-month new-graduate development program while designing, building, deploying, and operating data architectures and pipelines. Develops processes for capturing, transforming, storing, and maintaining structured and unstructured data across distributed and local systems. Applies automation, big-data, artificial intelligence, and machine-learning techniques to support data integrity, compatibility, and evolving business requirements.

Performs data-quality checks, migration validation, SQL-based reconciliation, discrepancy investigation, and root-cause analysis across large VA healthcare datasets. Supports DMM tools, cutover and go-live activities, reporting, application monitoring, access administration, documentation, and stakeholder meetings. Collaborates with VA, Oracle Health, InterSystems IRIS, engineering, and subject-matter teams to resolve migration issues and implement new data-quality capabilities.

Contribute to building and maintaining cloud-based data pipelines and data products. Develop, test, and optimize SQL and Python transformations, participate in code reviews, troubleshoot data quality issues, and follow CI/CD and documentation practices to support analytics and AI initiatives.