Senior Data Streaming Engineer

Bank of America•Jersey City, NJ

3d•$104,000 - $157,700•Onsite

About The Position

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day. Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve. Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us! Position Summary: This job is responsible for driving efforts to develop and deliver complex data solutions to accomplish technology and business goals. Key responsibilities include leading code design and delivery tasks with the integration, cleaning, transformation and control of data in operational and analytical data systems. Job expectations include liaising with vendors and working with stakeholders and Product and Software Engineering teams to implement data requirements, analyzing performance, and researching and troubleshooting issues within system engineering domains. Infrastructure Information Services is seeking a Senior Data Streaming Engineer to design and deliver a modern, real-time data platform supporting Infrastructure and ITSM domains. This role focuses on building scalable, low-latency data pipelines using event-driven architecture and stream processing technologies, while evolving legacy batch-oriented systems into a streaming-first ecosystem. The ideal candidate brings deep expertise in data engineering, stream processing, and data lakehouse architectures, with hands-on experience using Apache Flink, Confluent Kafka, and Apache Iceberg. This individual will help modernize data integration patterns, improve data freshness, and enable real-time analytics aligned with enterprise data management standards.

Requirements

10+ years of IT experience with strong focus on data engineering
5+ years in data warehousing, data lakes, or MDM systems
Strong experience with: Apache Flink (stream processing, event time, windowing, stateful processing)
Confluent Kafka (topics, partitions, schema registry, connectors)
Apache Iceberg (table format, partition evolution, ACID, time travel)
Proficiency with: Python, PySpark, or Java/Scala for data engineering
SQL (advanced querying and optimization)
Experience designing event-driven and streaming architectures
Deep understanding of data modeling for streaming and lakehouse systems
Experience working with Kafka-based ingestion patterns and CDC frameworks
Knowledge of data formats (Parquet, Avro) and optimization techniques
Experience integrating with RDBMS (Oracle, DB2, SQL Server) and migrating to modern platforms
Familiarity with Hadoop ecosystem components (Spark, Hive, HDFS) in hybrid environments
Experience with SDLC practices, CI/CD pipelines, and version control
Strong ability to translate design into production-grade implementations

Nice To Haves

Experience with real-time analytics use cases (monitoring, alerting, observability)
Hands-on experience with Kafka Connect, ksqlDB, or stream enrichment frameworks
Familiarity with data governance, lineage, and metadata management
Experience with cloud-based data platforms (AWS, Azure, or GCP)
Exposure to DevOps and infrastructure automation
Experience with Agile tools such as Jira and Bitbucket

Responsibilities

Leads story refinement and delivery of requirements through the delivery lifecycle and assists team members in resolving technical complexities
Codes complex solutions to integrate, clean, transform, and control data, builds processes supporting data transformation, data structures, metadata, data quality controls, dependency, and workload management, assembles complex data sets, and communicates required information for deployment
Leads documentation of system requirements, collaborates with development teams to understand data requirements and feasibility, and leverages architectural components to develop client requirements
Leads testing teams to develop test plans, contributes to existing test suites including integration, regression, and performance, analyzes test reports, identifies test issues and errors, and leads triage of underlying causes
Leads work efforts with technology partners and stakeholders to close gaps in data management standards adherence, negotiates paths forward by thinking outside the box to identify and communicate solutions to complex problems, and leverages knowledge of information systems, techniques, and processes
Leads complex information technology projects to ensure on-time delivery and adherence to release processes and risk management and defines and builds data pipelines to enable data-informed decision making
Mentors Data Engineers to enable continuous development and monitors key performance indicators and internal controls
Design and implement real-time data pipelines using Apache Flink and Confluent Kafka
Transform legacy batch and RDBMS-based data workflows into event-driven streaming architectures
Build and optimize streaming ingestion, transformation, and enrichment pipelines
Develop and maintain Iceberg-based data lakehouse tables to support both streaming and analytical workloads
Ensure data quality, reconciliation, and consistency across streaming and batch systems
Optimize performance of streaming jobs, including state management, checkpointing, and scalability
Design efficient partitioning, schema evolution, and storage strategies using Iceberg
Integrate data across multiple systems including ITSM platforms (e.g., ServiceNow)
Collaborate with architecture and platform teams to establish best practices for streaming data frameworks
Support CI/CD, deployment, and operational monitoring of streaming pipelines