Senior Data Engineering Lead

Citi•Jersey City, NJ

About The Position

Technical Leadership: Provide technical leadership and mentorship to a team of Data Engineers. Oversee the design, development, and optimization of scalable and robust big data solutions and pipelines. Big Data Engineering: Utilize extensive expertise in big data technologies such as Apache Spark (with Scala/Python), Apache Kafka, and distributed data processing frameworks. Design, develop, and implement highly performant and fault-tolerant ETL/ELT pipelines for large-scale data ingestion, processing, and transformation. System Analysis and Design: Partner with multiple management teams to ensure appropriate integration of functions to meet goals. Identify and define necessary system enhancements to deploy new products and process improvements. Conduct in-depth analysis of complex business processes and system processes to recommend innovative solutions. Project Management: Manage and implement successful projects, ensuring adherence to timelines, budget, and quality standards. Utilize project management methodologies (e.g., Scaled Agile Framework (SAFe) using JIRA) to drive project execution. Code Quality and Standards: Ensure application design adheres to the overall architecture blueprint. Develop and enforce standards for coding, testing, debugging, and implementation. Conduct code reviews to ensure code quality and compliance with standards. Collaboration and Communication: Collaborate with cross-functional teams, including architects, infrastructure engineers, and business analysts, to deliver integrated solutions. Consistently demonstrate clear and concise written and verbal communication. Mentoring and Coaching: Serve as an advisor or coach to mid-level developers and analysts, allocating work as necessary and providing guidance on technical best practices. Drive compliance with applicable laws, rules, and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct, and business practices, and escalating, managing, and reporting control issues with transparency. DevOps Practices: Implement and promote DevOps practices, including continuous integration, continuous delivery, and automated testing. Containerization and Orchestration: Utilize Openshift for container orchestration, ensuring applications are scalable, resilient, and easily deployed. Version Control: Manage source code using GitHub, following established branching strategies and code review processes.

Requirements

10+ years of progressive technology experience in banking or financial services, including 5+ years of hands-on delivery leadership across enterprise-grade platforms.
Proven success managing large-scale, cross-functional Agile delivery in regulated environments with a strong track record of engineering excellence.
Solid academic foundation in Computer Science or Engineering
Strong hands-on experience in solutioning and delivering modern architectures, including microservices, cloud-native APIs, and secure data pipelines.
Deep working knowledge of Agile/SAFe delivery frameworks, sprint planning, PI planning, and DevSecOps automation (CI/CD, GitOps, release pipelines).
Practical exposure to Generative AI and LLM-based tools, with ability to drive automation, code acceleration, or intelligent query/document processing.
Experience leading production-grade implementations on hybrid/multi-cloud platforms, including failover, monitoring, and resiliency engineering.
Effective communicator and influencer across business, engineering, and architecture; adept at connecting GenAI innovation to business outcomes.
Bachelor's degree/University degree or equivalent experience
Academic foundation in Computer Science or Engineering

Nice To Haves

certifications in Agile/SAFe, Cloud, or GenAI/ML frameworks are a strong plus.

Responsibilities

Provide technical leadership and mentorship to a team of Data Engineers.
Oversee the design, development, and optimization of scalable and robust big data solutions and pipelines.
Utilize extensive expertise in big data technologies such as Apache Spark (with Scala/Python), Apache Kafka, and distributed data processing frameworks.
Design, develop, and implement highly performant and fault-tolerant ETL/ELT pipelines for large-scale data ingestion, processing, and transformation.
Partner with multiple management teams to ensure appropriate integration of functions to meet goals.
Identify and define necessary system enhancements to deploy new products and process improvements.
Conduct in-depth analysis of complex business processes and system processes to recommend innovative solutions.
Manage and implement successful projects, ensuring adherence to timelines, budget, and quality standards.
Utilize project management methodologies (e.g., Scaled Agile Framework (SAFe) using JIRA) to drive project execution.
Ensure application design adheres to the overall architecture blueprint.
Develop and enforce standards for coding, testing, debugging, and implementation.
Conduct code reviews to ensure code quality and compliance with standards.
Collaborate with cross-functional teams, including architects, infrastructure engineers, and business analysts, to deliver integrated solutions.
Consistently demonstrate clear and concise written and verbal communication.
Serve as an advisor or coach to mid-level developers and analysts, allocating work as necessary and providing guidance on technical best practices.
Drive compliance with applicable laws, rules, and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct, and business practices, and escalating, managing, and reporting control issues with transparency.
Implement and promote DevOps practices, including continuous integration, continuous delivery, and automated testing.
Utilize Openshift for container orchestration, ensuring applications are scalable, resilient, and easily deployed.
Manage source code using GitHub, following established branching strategies and code review processes.