Data Engineer

PNC Financial Services Group is hiring a Data Engineer to join its Data and Automation organization. This onsite role in Phoenix, AZ supports large-scale enterprise big data platforms and focuses on building cloud-native, modern data solutions that help translate analytics needs into technical outcomes.

You will contribute across the platform lifecycle, including health and observability, ETL/ELT monitoring, modernization of legacy systems, and migration toward a unified data platform.

What you’ll do

• Monitor, maintain, and improve the health and performance of the enterprise data platform
• Support stability, reliability, and scalability of data systems
• Apply platform operations best practices and data engineering standards, ensuring alignment across technical and business customers
• Conduct frequent reviews of data platform architecture and identify opportunities for automation, optimization, right-sizing, and cost reduction
• Drive user migration to a unified data platform
• Modernize legacy Hadoop-based systems into cloud-native architectures
• Design and build scalable data observability analytics to support site reliability
• Develop ETL/ELT pipeline monitoring solutions to detect bottlenecks, failures, and abnormal processing metrics
• Create intelligent alerts to identify high-utilization Spark and Trino jobs to protect service ability for users
• Capture and report compute usage across Kubernetes, Docker, and other containers for job and query optimization visibility
• Build dashboards and visualizations for real-time visibility into platform health, performance, and availability
• Partner with business stakeholders to translate analytical needs into technical solutions
• Act as an internal consultant for big data tool and capability guidance
• Enhance query performance and optimize data structures to support analytics
• Standardize the use of AI and Generative AI tools for automation, development acceleration, and improved platform efficiency and governance
• Use code generation for query and cost optimization to maximize platform resources
• Engineer solutions using vector databases, embedding pipelines, feature stores, and Retrieval-Augmented Generation workflows
• Maintain engineering code and data integrity through automated code generation, profiling, and data cleansing tools
• Implement internal process improvements using detailed analytics tied to key business performance metrics
• Develop, support, and implement data services for multiple applications, including data preparation process development
• Design and build data service infrastructure across multiple data platforms according to business processes and workflows
• Implement data migration and transformation activities to ensure accuracy and security

Requirements

• 3+ years of relevant, direct industry experience (roles at this level typically require a university/college degree)
• Bachelor’s degree
• Strong proficiency in Python, Spark, and Scala
• Practical experience with SQL development and enterprise database systems such as Oracle and Teradata
• Experience with modern, cloud-native and open-source tooling, including Apache Iceberg, Apache Airflow, and Trino (highly valued)
• Strong experience with scalable data platforms and analytical solutions using data Lakehouse architecture
• Knowledge and experience with machine learning concepts for automation development
• Familiarity with cloud platforms such as Azure and AWS, including data-related services
• Experience with data observability tools (Monte Carlo, Acceldata) or monitoring stacks (Datadog, Grafana, Prometheus) is a plus
• Ability to go beyond data movement and contribute to solution design
• Strong communication skills to translate between technical and non-technical audiences
• Proven ability to bridge business and engineering teams, with a collaborative mindset across cross-functional teams

Technology areas you’ll use

• Python, Spark, Scala, SQL
• Oracle, Teradata
• Apache Iceberg, Apache Airflow, Trino, Hadoop
• Cloud-native services (Azure, AWS)
• Monitoring and observability: Monte Carlo, Acceldata, Datadog, Grafana, Prometheus
• Compute and orchestration: Kubernetes, Docker
• ETL, ELT, data cleansing
• Vector databases, embedding pipelines, feature stores, Retrieval-Augmented Generation
• AI, Generative AI, and data observability analytics
• Data Lakehouse architecture

Compensation

• Base salary: $86,250.00 - $158,125.00 per year
• Salaries may vary based on geographic location, market data, and individual skills, experience, and education
• This role is incentive eligible, with payment based on company, business, and/or individual performance

Benefits

• Medical/prescription drug coverage with a Health Savings Account feature
• Dental and vision options
• Employee and spouse/child life insurance
• Short and long-term disability protection
• 401(k) with PNC match
• Pension and stock purchase plans
• Dependent care reimbursement account
• Back-up child/elder care
• Adoption, surrogacy, and doula reimbursement
• Educational assistance, including select programs fully paid
• Robust wellness program with financial incentives
• Maternity and/or parental leave
• Up to 11 paid holidays each year
• 9 occasional absence days each year, unless otherwise required by law
• Between 15 to 25 vacation days each year, depending on career level
• Years of service

Application window

Generally expected to be posted for two business days from 11/09/2026, though it may extend based on business discretion.