Senior Data Engineer IRC306110

GlobalLogic is a digital engineering partner that helps companies create digital products, platforms, and services. The Senior Data Engineer will design, build, deploy, and support scalable batch and streaming data pipelines, integrate diverse data sources, and develop curated datasets using SQL, Spark, and cloud data services. The role also focuses on data quality, reliability, security, monitoring, troubleshooting, documentation, and AI-enabled engineering automation.

Responsibilities:

• 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

Requirements:

• 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