Principal Data Engineer - FLINK

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.

- 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.

- 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

- 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

- 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 related discipline - Certifications in Big Data, AWS, Streaming Technologies, or Agile methodologies preferred

- Champion shift from batch-centric architectures to event-driven, streaming-first platforms - Define and implement enterprise streaming architecture patterns - Establish standards for data contracts, schema evolution, and event governance - Build scalable, cloud-native streaming platforms aligned to enterprise architecture strategy - Integrate streaming with AI/ML platforms to enable real-time inference and intelligent automation - Drive platform reliability and engineering maturity, including automated testing, CI/CD, and infrastructure-as-code for streaming pipelines - Promote reusability and modular design in streaming components to accelerate delivery across teams - Ensure all solutions meet security, compliance, and risk requirements specific to financial institutions