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# Atlassian Developer (Jira, Confluence) IRC305981

- **Company:** GlobalLogic
- **Location:** Charlotte, NC, United States (Remote available)
- **Experience:** Expert
- **Salary:** $120,000.0 - $130,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Confluence, JIRA, BigQuery, Cloud Database, Information Systems, Databases, Continuous Integration, Database Queries, Software Debugging, Distributed Data Store, Document-Oriented Databases, Python (Programming Language), Performance Tuning, Software Tools, Cloud Services, Scala (Programming Language), SQL Databases, Data Streaming, Workflow Management Systems, Software Organization, Google Cloud, Retrieval-Augmented Generation, Apache Spark, IT Architecture, AI Coding Agents, Information Technology, Atlassian Tools, Azure Synapse Analytics, Software Version Control, Data Pipelines, Programming Languages
- **Published:** October 9, 2026
- **Apply:** https://www.careerjet.com/job/us0cbd43a58dd1538b8bdf0a8ce6f290f9/eaa

## About the Role

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field; equivalent relevant experience may be considered.
Experience building and maintaining data pipelines using Python, Java, Scala, or similar programming languages.
Strong SQL skills and experience with relational and analytical databases.
Experience with Apache Spark and distributed data-processing frameworks.
Experience with cloud data platforms, preferably Google Cloud Platform and BigQuery or Azure Synapse.
Experience with orchestration tools such as Apache Airflow.
Knowledge of software development best practices, including version control, testing, CI/CD, and Agile delivery.
Demonstrated, hands-on experience using approved AI coding tools on a day-to-day basis to develop, test, debug, document, and improve software and data pipelines; able to critically validate AI-generated output for accuracy, security, privacy, and maintainability.
High-level knowledge of AI architecture and the AI application lifecycle, including foundation models, prompts and context, retrieval-augmented generation, tool or agent integration, evaluation, observability, governance, and responsible-use considerations. Able to explain why an AI-assisted approach is appropriate for a given engineering problem and when it is not.
Highly self-driven and accountable, with the ability to independently clarify goals, prioritize work, make sound technical decisions, and deliver outcomes with minimal day-to-day direction from leadership. Proactively identifies risks, opportunities, and needed stakeholder alignment.

## Description

GlobalLogic estimates the starting base salary for the remote Senior Data Engineer position in the USA to be between $120000 to $130000 and reflects base salary only and does not include additional performance-linked variable compensation, benefits etc that may be applicable for the role. This pay range is provided as a good faith estimate and the amount offered may be higher or lower. GlobalLogic takes many factors into consideration in making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations., Design, build, test, deploy, and support batch and streaming data pipelines.
Integrate data from databases, files, APIs, event streams, and cloud platforms.
Develop scalable transformations and curated datasets using SQL, Spark, and cloud data services.
Collaborate with product owners, analysts, and business partners to translate requirements into technical solutions.
Implement data-quality checks, monitoring, alerting, security controls, and performance optimizations.
Participate in Agile ceremonies, provide delivery updates, and contribute to end-to-end feature ownership.
Use approved AI-assisted engineering tools in day-to-day development to accelerate coding, testing, documentation, troubleshooting, and process improvement, while validating outputs for accuracy, security, privacy, and maintainability.
Identify and implement practical AI-enabled automation opportunities that improve data-engineering productivity, quality, and delivery speed.
Troubleshoot production issues and improve pipeline reliability, maintainability, and cost efficiency.
Document data flows, technical designs, dependencies, and operational procedures.