Job DescriptionPrincipal Data Engineer - Real-Time Streaming (Flink)Role SummaryAs 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 related discipline
- Certifications in Big Data, AWS, Streaming Technologies, or Agile methodologies preferred
Modernization and Architecture Expectations- 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
