Analyst, Data Engineering

The Data & Analytics Function oversees the analytics lifecycle to identify, analyze, and present actionable insights that drive business decisions and create competitive advantage. This function manages enterprise data platforms, data access, governance, reporting, business intelligence solutions, and advanced analytics capabilities.

Data Engineering is responsible for building, maintaining, and optimizing scalable data platforms that enable efficient data ingestion, transformation, orchestration, and consumption across the enterprise. As part of the Platform Engineering team, this role focuses on delivering reliable, secure, automated, and scalable data infrastructure supporting business-critical analytics and data products.

The ideal candidate will contribute to platform operations, cloud-based data engineering solutions, CI/CD automation, and production support while partnering with cross-functional teams to ensure a stable and high-performing data ecosystem.

Qualifications

• 1-3 years of experience in Data Engineering, Platform Engineering, Site Reliability Engineering (SRE), DevOps, or related technical roles.
• Bachelors degree in computer science, Engineering, Information Systems, or a related discipline (or equivalent work experience).
• Bachelors degree in computer science, Engineering, Information Systems, or a related field.
• Hands-on experience with Databricks is required.
• Experience working with cloud platforms such as Azure, AWS, or GCP is required.
• Experience using CI/CD tools such as Harness, Azure DevOps, GitHub Actions, or equivalent is required.
• Experience with SQL, Python, and basic PySpark development.
• Experience with Git/GitHub or other source control tools is required.
• Understanding of Apache Spark fundamentals.
• Familiarity with monitoring, troubleshooting, and production support activities.
• Basic knowledge of Infrastructure as Code (Terraform preferred) is a plus.

Key Responsibilities

• Support daily operations of enterprise data platforms and Databricks environments.
• Develop and maintain basic Databricks workloads using SQL, Python, and PySpark.
• Assist in maintaining CI/CD deployment pipelines and release processes.
• Monitor platform performance and identify issues impacting data services.
• Participate in incident management and support activities.
• Troubleshoot data platform and deployment issues with guidance from senior team members.
• Follow security, governance, and operational best practices.
• Maintain technical documentation and operational runbooks.
• Collaborate with Data Engineers, Architects, and Platform teams on assigned initiatives.

What Is Expected of You and Others at This Level

• Applies foundational technical knowledge and engineering best practices to platform and data engineering tasks.
• Works on projects of moderate scope under guidance from senior engineers and technical leads.
• Troubleshoots and resolves technical issues using established procedures and operational frameworks.
• Demonstrates strong analytical and problem-solving capabilities.
• Continuously develops expertise in Databricks, cloud technologies, DevOps, and platform engineering practices.
• Supports platform reliability, automation, and operational excellence initiatives.
• Effectively collaborates within cross-functional and geographically distributed teams.
• Takes ownership of assigned deliverables and contributes to team success.

Preferred Technical Skills

• Databricks (Workflows, Delta Lake, Unity Catalog)
• Apache Spark / PySpark
• Harness CI/CD Pipelines
• Python
• SQL
• Azure Data Platform, GCP, or AWS
• Git/GitHub
• Terraform
• Monitoring and Observability Tools
• DevOps & Platform Engineering Practices
• Incident and Production Support Management

Work Schedule

This role is part of the global Data Engineering Platform Operations team and requires participation in a rotational shift schedule to ensure support coverage across multiple regions and time zones. Team members will also be required to provide weekend support on a rotational basis, approximately one weekend per month. A compensatory day off will be provided for weekend support.

Candidates should be comfortable working in the following shifts based on business requirements:

• Morning Shift: 7:00 AM - 3:00 PM
• Afternoon Shift: 1:00 PM - 10:00 PM
• Evening Shift: 5:30 PM - 2:30 AM

The shift schedule may vary depending on operational and business needs.

Additional Expectations

• Participate in rotational production support and platform operations activities.
• Monitor and support Databricks environments, data pipelines, CI/CD deployments, and platform services.
• Respond to platform incidents, service disruptions, deployment failures, and operational requests within defined SLAs.
• Collaborate effectively with globally distributed platforms, engineering, and business teams.
• Support continuous improvement initiatives focused on automation, reliability, and operational efficiency.

This role offers an excellent opportunity to gain hands-on experience with modern data platforms, cloud technologies, Databricks, DevOps practices, and enterprise-scale data engineering operations.