Principal Data Engineer - FLINK

Principal Data Engineer – Real-Time Streaming (Flink)

Role Summary

As a Principal Data Engineer (Real-Time Streaming – Flink), you will be chartered with designing, developing, and operating real-time data systems that drive critical business outcomes. You will lead a team of data engineers and partner with stakeholders to build scalable, event-driven streaming architectures that enable low-latency data access across Citizens business operations.

In addition to core data engineering responsibilities, this role emphasizes Flink-based streaming platforms, event-driven data flow, and highly resilient distributed systems, ensuring that data is continuously processed, governed, and made actionable in near real time.

Specialized Responsibilities

• Serve as a key contributor to the development of real-time data solutions, partnering with stakeholders to define streaming use cases, SLAs, and latency expectations. 
• Design and implement event-driven streaming architectures using  Flink and related ecosystem technologies.
• Engineer and optimize low-latency, high-throughput data pipelines for operational and analytical workloads.
• Develop and maintain stateful stream processing applications, including windowing, joins, aggregations, and complex event processing.
• Continuously assess data flow across systems, identifying latency bottlenecks, failure points, and data integrity risks, with a focus on real-time processing gaps. 
• Implement observability, monitoring, and alerting for streaming systems to ensure availability, performance, and SLA adherence.
• Ensure operational resiliency and stability, including checkpointing, fault tolerance, exactly-once semantics, and recovery strategies in Flink pipelines.
• Lead the development of streaming data models and schemas aligned to business outcomes and event contracts. 
• Govern and evolve event schemas and contracts to support enterprise-wide interoperability and data consistency.
• Guide engineering teams on best practices for distributed streaming systems, including back-pressure management, scaling, and partitioning strategies.
• Partner with architecture and platform teams to define standards for real-time data platforms, security, and regulatory compliance within a banking environment.
• Mentor engineers and drive adoption of streaming-first design patterns within Agile delivery teams.

Preferred Technical Expertise

• Advanced expertise in  Flink 
• Strong experience with event streaming platforms
• Deep understanding of distributed systems design, including fault tolerance, scaling, and high availability
• Experience building stateful stream processing pipelines with windowing, joins, and event-time processing
• Proficiency in low-latency pipeline design and performance optimization
• Experience with cloud-native streaming architectures 
• Strong programming skills in Java, Scala, and/or Python with streaming frameworks 
• Familiarity with schema management 
• Experience integrating streaming data with downstream systems (data lakes, data warehouses, APIs, analytics platforms)
• Knowledge of real-time analytics and monitoring tools 
• Understanding of data governance, lineage, and compliance in real-time data environments

Business Outcomes and Impact

• Enable real-time decision-making across banking operations
• Reduce data latency from hours to seconds/minutes, improving responsiveness of business processes
• Improve data reliability and trust through resilient, fault-tolerant streaming pipelines
• Support digital and event-driven business models, including real-time customer experiences
• Increase operational efficiency by unifying batch and streaming data architectures
• Strengthen regulatory and risk capabilities through timely and accurate data availability
• Drive enterprise scalability, enabling growth in transaction volumes and data complexity

Preferred Qualifications

• 8+ years of data engineering experience with demonstrated leadership in streaming data platforms 
• Hands-on experience implementing  Flink in production environments
• Experience in financial services or banking, with understanding of real-time data use cases such as payments, fraud, or trading 
• Experience managing or mentoring engineering teams in Agile delivery environments 
• Familiarity with machine learning integration in streaming pipelines (real-time scoring/inference) 
• Experience with BI and analytics tools to consume streaming outputs 
• Bachelor’s degree required; Master’s preferred in Computer Science, Engineering, or relat