Principal Data Engineer - FLINK

Citizens•Johnston, RI

14d

About The Position

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.

Requirements

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
Bachelor’s degree required; Master’s preferred in Computer Science, Engineering, or related discipline

Nice To Haves

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

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.
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.