Staff Data Engineer

Who we are looking for

The State Street Security Architecture, Analytics & Fusion Engineering (SA2FE) team is looking for a Staff Data Engineer, Integrations Lead . The Fusion Analytics and Data Engineering team delivers models, insights, and tooling to help Cybersecurity teams make faster, more informed decisions as we work to secure State Street’s digital footprint. As a Data/Analytics Engineer, you will develop the data flows, analytics pipelines, and production machine-learning systems -- in collaboration with data product managers, architects, engineers, and other team members -- to create analytics & ML-driven data products that support our mission to build predictive models and intelligent systems that help secure State Street’s information and infrastructure. This is a unique greenfield project and we are looking for an experienced technical leader with broad experience in architecture, operations, and engineering to help design and deliver a model platform, and lead and mentor a growing team of cybersecurity data professionals.

Due to the role requirements this job needs to be performed primarily in the office with some flex work opportunities available.

What you will be responsible for

As a Staff Cyber Data Engineer, Integrations Lead you will

• Design and build global distributed petabyte scale data-mesh systems for high availability, high throughput, data consistency, security, and privacy, defining our next generation of security data analytics tooling.  
• Use your understanding of large scale data processing and analytics to wrangle our unique cybersecurity data and create analyses and tools that point to the most significant business, governance, and risk management impacts.
• Design and build petabyte scale systems for high availability, high throughput, data consistency, security, and end user privacy, defining our next generation of data analytics tooling
• Build data modeling and ELT workflows to produce Raw, Rationalized, co-Related, and Reporting data flows for graph, timeseries, structured, and semi-structured cybersecurity data
• Work alongside the global cybersecurity architecture & engineering leadership to develop and deliver capabilities to support Cyber Data Science initiatives both internally and in partnership with detection and response teams, and governance and risk management teams across our CISO organization.
• Mentor and train engineers and architects, and build out a Data Mesh, Lakehouse, Kappa Streaming and Data Security Architecture practice.

What we value

These skills will help you succeed in this role

• 10+ years of experience with Python, Java, or similar languages, with cloud infrastructure (e.g. AWS, GCP, Azure), and deep experience working with big data processing infrastructures and ELT orchestration
• Experience developing distributed batch and real-time feature stores, and developing coordinated batch, streaming and online model execution workflows, building and optimizing large scale data processing jobs in Spark, GraphX/GraphFrames, Spark Structured Streaming, as well as scaling graph and time-series native operations.
• Experience with designing for data lineage, federation, governance, compliance, security, and privacy — hands on experience with commercial DataSecOps platforms like Immuta, Satori and/or experience building custom access control (RBAC/ABAC), data masking, tokenization, and FPE systems for cloud data lake environments. Experience with globally distributed federated data systems is highly desirable.
• Experience with data quality monitoring and with building continuous data pipelines and implementing history and time-travel using modern data lake storage layers like Delta Lake, Iceberg, and LakeFS
• Experience with MLOps and iterative cycles of end-to-end development, MRM coordination, deployment, and monitoring of production grade ML models in a regulated high-growth tech environment5+ years of experience with Python, Java, or similar languages, with cloud infrastructure (e.g. AWS, GCP, Azure), and deep experience working with big data processing infrastructures and ELT orchestration

Education & Preferred Qualifications

• B.S., M.S., or PhD. in Computer Science or equivalent work experience
• 8+ years of experience building large scale distributed systems and data analytics processes on cloud native, in-memory, and fit-for-purpose hybrid infrastructure. Experience with cybersecurity data and globally distributed log & event processing systems with data mesh and data federation as the architectural core is highly desirable.