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# Data Engineers

- **Company:** Everforth Apex
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Unity 3d, Web Interfaces, Java (Programming Language), Microsoft Azure, Databases, Continuous Integration, Data Architecture, Data Validation, Data Control, Information Engineering, Data Integrity, Extract Transform Load (ETL), Relational Databases, Middleware, Github, Apache Hive, Spring Framework, Python (Programming Language), PostgreSQL, Message Broker, Microsoft SQL Server, Object-Oriented Software Development, Oracle (Applications), Power BI, Software Deployment, Software Engineering, PL-SQL, Tableau (Software), Data Logging, Scripting, Enterprise Software Applications, Sql Optimization, Spring-boot, Data Layers, Pandas, Build Management, Data Lakes, Pyspark, Kubernetes, Information Technology, Apache Kafka, Data Lakehouse, Restful APIs, Angular Ui, Data Pipelines, Docker, Jenkins, Databricks, Microservices
- **Published:** October 30, 2026
- **Apply:** https://www.dice.com/job-detail/a732ce78-04c6-4754-98cf-81cd90189d77

## About the Role

Education: A bachelor's degree in Computer Science, Information Technology, or a related technical discipline is preferred.

Experience: Six to eight plus years of progressive software engineering and data engineering experience in enterprise environments, including a proven track record of implementing legacy-to-cloud data migration utilities and multi-source ETL pipelines.

Technical Skills: Deep hands-on experience developing in Databricks, Delta Lake, Unity Catalog, Databricks Workflows/Jobs, and Spark SQL. Advanced proficiency in Python (PySpark, modular OOP scripting) and strong core Java proficiency (Spring Boot, REST APIs, or batch integration). Advanced SQL and PL/SQL capabilities across relational databases such as Oracle, SQL Server, and PostgreSQL are required, along with proficiency in GitHub and automated CI/CD pipelines.

Preferred Qualifications

 * Hands-on exposure to Kubernetes or Docker containers for microservices and utility hosting.
 * Familiarity with Event Hub, Kafka, or message broker patterns for near-real-time ingestion feeds.
 * Basic understanding of Angular UI or similar web interfaces for administrative consoles and utility monitoring frontends.
 * Practical experience delivering reporting models in Databricks SQL, Tableau, or Power BI.

## Description

This professional engagement requires two senior data engineering professionals to accelerate the development, validation, and production rollout of enterprise data migration utilities and analytical pipelines. Selected candidates will design and build robust, Python- and Java-based ETL pipelines to ingest, format, and harmonize data from multiple source systems into Databricks (Lakehouse/Delta Lake). The position involves developing downstream analytics and business intelligence reports while collaborating cross-functionally across multiple interface and platform teams to gather requirements, conduct data validation, and ensure smooth operational productionalizing., * Design, build, and optimize enterprise-grade ETL/ELT pipelines and utilities using Python (PySpark, Pandas) and Java (Spring Framework/Core Java).

 * Extract, transform, and clean structured and semi-structured datasets from diverse databases and enterprise applications.
 * Ingest and catalog data into Databricks Delta Lake, ensuring performance, data integrity, and compliance with data architecture standards.
 * Build semantic data layers, views, and aggregate tables within Databricks to power business intelligence dashboards and operational reporting.
 * Develop and deliver analytical reporting solutions, including Databricks SQL Dashboards, Power BI, or enterprise equivalents, for stakeholders.
 * Partner with upstream source system owners, interface teams, and platform engineering groups to capture requirements, schema definitions, and validation criteria.
 * Coordinate end-to-end integration testing, data reconciliation, and sign-offs between legacy source platforms and modern target platforms.
 * Operationalize data pipelines by establishing robust error handling, automated retry logic, logging, and data monitoring.
 * Package and deploy code through enterprise CI/CD workflows utilizing GitHub Actions, Jenkins, Azure DevOps, or ArgoCD.
 * Troubleshoot complex end-to-end issues across application, middleware, data lakehouse, and infrastructure layers.