Data Engineer II

About the Role

We are seeking a Data Engineer II to join our Global Data Platform & Engineering team. In this role, you will help build and maintain scalable, cloud-native data solutions that power enterprise analytics, reporting, and emerging AI initiatives.

You will work with modern data technologies including Databricks, Apache Spark, Delta Lake, Python, SQL, and AWS to develop reliable data pipelines and curated datasets. Working closely with business stakeholders, analytics teams, and fellow engineers, you'll transform business requirements into high-quality, production-ready data solutions.

What You'll Do

• Design, develop, and maintain scalable ELT/ETL pipelines using Databricks, Apache Spark, SQL, and Python. 
• Build and maintain reliable, high-quality datasets that support enterprise analytics, operational reporting, and AI use cases. 
• Develop data transformations using Delta Lake and Lakehouse architecture best practices. 
• Integrate data from enterprise applications, APIs, databases, files, and other internal and external sources. 
• Collaborate with business stakeholders, analysts, and product teams to understand data requirements and deliver scalable solutions. 
• Monitor, troubleshoot, and optimize production data pipelines for reliability, scalability, and performance. 
• Participate in migrating and modernizing legacy data processes to cloud-native architectures. 
• Improve data quality through validation, testing, monitoring, and automation. 
• Contribute to engineering best practices through code reviews, documentation, and knowledge sharing. 
• Support continuous improvement of the enterprise data platform by identifying opportunities for automation, optimization, and simplification.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience. 
• 3+ years of experience in Data Engineering or a related technical role. 
• Strong SQL development skills. 
• Strong Python programming experience. 
• Experience building data pipelines using Apache Spark and Databricks (or similar distributed data processing platforms). 
• Experience developing modern ETL/ELT pipelines. 
• Understanding of data modeling and data warehousing concepts. 
• Experience working with cloud-based data platforms, preferably AWS. 
• Familiarity with Delta Lake or similar modern data storage technologies. 
• Experience using Git and collaborative software development practices. 
• Strong analytical, troubleshooting, and problem-solving skills. 
• Excellent communication and collaboration skills.

Preferred Qualifications

• Experience with AWS data services such as Amazon S3, Lambda, or related technologies.
• Experience working with Databricks on AWS.
• Experience with Delta Lake and Medallion/Lakehouse architecture.
• Experience with orchestration tools such as Databricks Workflows, Apache Airflow, or similar workflow orchestration platforms. 
• Experience consuming and developing REST APIs. 
• Experience working with semi-structured data, including JSON and Excel-based data ingestion. 
• Familiarity with CI/CD practices for data engineering. 
• Exposure to AI/ML workflows or data preparation for machine learning and generative AI applications. 
• Experience working in gaming, hospitality, financial services, or other data-intensive industries.

Technical Environment

You'll work with technologies including: 

• Databricks (AWS) 
• Apache Spark 
• Delta Lake 
• Python 
• SQL 
• Amazon S3 
• Git 
• REST APIs 
• CI/CD pipelines

What Makes You Successful

• You enjoy solving complex technical problems and continuously improving existing solutions. 
• You can work independently while collaborating effectively with cross-functional teams. 
• You write clean, maintainable, and well-tested code. 
• You take ownership of your work from development through production support. 
• You have a growth mindset and enjoy learning new technologies. 
• You communicate technical concepts clearly with both technical and non-technical audiences.

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