The role
From State Street Bank and Trust's own posting.
Role Purpose
The Senior Data Platform Architect is responsible for the end‑to‑end architecture, security design, and governance of SSIM's enterprise data platform spanning Snowflake, Databricks, open data platforms (Iceberg/Parquet/Delta), and data virtualization. Requires previous experience supporting workloads in analytics, regulatory reporting, AI/ML, and batch applications.
The person in this role will ensure the modern platform meets financial‑services regulatory expectations, enterprise security standards (GCS/IAM), and data protection requirements (NPPI/PII). They Will Enable scalable, interoperable delivery across Investment Management use cases.
This role is hands‑on, decision‑critical, and risk‑bearing, with accountability for architectural choices that directly impact regulatory compliance, audit defensibility, and business continuity. Platform Designs must be Secure, Scalable, include Disaster Recovery, as well as Data Sharing capabilities. Design and Build monitoring as well as chargeback models for billing back to application teams.
Core Responsibilities
1. Data Platform Architecture & Integration
Define and govern the hybrid Snowflake–Databricks architecture to support classic Data Warehousing as well as Data Lake designs, aligning Snowflake's governed analytics layer with Databricks' engineering, streaming, and ML capabilities
Establish reference blueprints, patterns using lakehouse and medallion patterns suited for enterprise scale and auditability.
Contribute Designs, Patterns, Assets to SSIM Center of Excellence which establishes Best Practices for each platform as well as use cases for Reuse.
Lead Iceberg/Horizon/Unity Catalog interoperability across AWS, Azure, and GCP
2. Open Platforms & Open Table Formats
Architect open data platform strategy leveraging Apache Iceberg, Delta Lake, Parquet, and Polaris/Unity Catalog to avoid vendor lock‑in and enable cross‑engine query
Define standards for open table format adoption, including:
Business Tool Access Protocols, Drivers, Standards
Naming Standards, Folder Structures, Cloud Storage Security
Schema evolution, partitioning, and time‑travel
Cross‑engine compatibility (Snowflake, Databricks, Trino, Spark, Athena)
Catalog federation across Horizon, Unity, Glue, and Polaris
Establish governance for open formats in regulated workloads (lineage, access control, encryption)
Drive interoperability between proprietary and open ecosystems to support modernization and exit strategies
Capture Feedback, Fialings, Suggestions form the Users an Provide that Distilled information back to Vendors to Improve their Offerings
3. Data Virtualization & Federated Access
Architect data virtualization patterns to enable logical access to data across Snowflake, Databricks, Oracle, SQL Server, and legacy NAS/file systems without physical movement
Define use cases for:
Federated queries (Snowflake external tables, Databricks Lakehouse Federation, Trino/Starburst, Denodo)
Virtual data products for analytics and reporting
Logical data fabric spanning cloud and on‑prem sources
Establish guardrails for performance, cost, security, and data classification in virtualized access patterns
Reduce data duplication, copy proliferation, and storage cost through virtualization where appropriate
4. Batch Application Solutioning & Modernization
Lead architecture and solutioning for batch applications migrating from legacy platforms (Oracle, mainframe, Unix/Linux ETL, file‑based workflows) to Snowflake/Databricks
Define patterns for:
Batch ingestion and orchestration (Airflow, Databricks Workflows, Snowflake Tasks/Streams, Control‑M integration)
Window/SLA‑driven batch processing with monitoring and rerun safety
Migration of legacy stored procedures, PL/SQL, and shell‑based ETL to cloud‑native equivalents
Hybrid batch + streaming patterns (medallion, change data capture)
Establish batch performance, cost, and resiliency standards, including Reprocessing, Transaction Rollback, Event Logging, Query Timeout Limits, Warehouse Credit Limits, Finding/Flagging Expensive Processes from Account Usage Tables.
Partner with application owners to retire legacy infrastructure as part of data center exit and cloud migration programs. Previous Redshift, DB2, SQL Server experience is a help for migrating off of these platforms and data conversion issues that arise.
Identify Out of Date Drivers and Application Versions that could be Security Risks, and Advise Applications Teams what to upgrade to, so teams modernize while migrating.
5. Security, Governance & Regulatory Alignment
Architect and enforce data security by design, including:
Design RBAC Roles at a Group Level for R, RW, Admin at the DB, Schema, Object Levels. This should balance security needs with what is reasonable overhead to Administrate.
Advise on Lower Level Row Access Policy, Column Masking, Secure Views so App Teams have row‑level and column‑level security where necessary.
Familiar with Establishing Future Grants
Knowledge of Managed Access Schemas and trade offs.
Knowledge of Network Address White Listing.
Knowledge of Cloud Level Roles and Security Integrations and how they enable data access with Snowflake Horizon and Databricks Unity Catalog additional features.
Ensure compliance with SOX, MRAs, internal/external audits, and data protection controls
Maintain defensible audit trails, lineage, and forensic traceability
Align with GCS data residency, isolation, and classification standards
6. Multi‑Cloud Platform Engineering (AWS / Azure / GCP)
Architect cloud‑native integrations across S3, ADLS, GCS, IAM, networking, and private link patterns
Drive cross‑region/cross‑cloud HA/DR designs including Snowgrid replication and Iceberg federation
Ensure vendor security, networking real‑time/streaming, and disaster recovery are embedded in design
7. Delivery, SME Leadership & Stakeholder Engagement
Act as SME for Snowflake, Databricks, open platforms, virtualization, and batch modernization during architecture reviews and escalations
Partner with Security (GCS/GTS), Risk, Audit, Application, and Cloud Engineering teams
Produce architecture diagrams, design artifacts, executive‑level presentations, and access models
Required Skills & Experience
Snowflake – WH Sizing, Object Hierarchy, Snowsight, RBAC, Horizon Catalog, governance, performance, data sharing, External Tables, Stages, Loading, SPs, Scripts, Snowpark
Databricks – Spark, Unity Catalog, ML pipelines, Delta/Iceberg, Lakehouse Federation
Open platforms – Apache Iceberg, Delta Lake, Parquet, Polaris, Trino/Starburst
Data virtualization – Denodo, Starburst, Snowflake/Databricks federation, logical data fabric
Batch modernization – Migration from Oracle/PL‑SQL, mainframe, Unix ETL, Control‑M to cloud‑native orchestration
Multi‑cloud – AWS, Azure, GCP (storage, IAM, networking, private link)
Data modeling – Kimbal, Denormalization, Dimensional, medallion schema patterns, bridge tables, marts, PK, FK, Clustering, and Tuning Implications
Languages – SQL, Python, Java, Javascript, Scala, Spark, Notebooks, Snowpark
LINUX – Must Know Linux, comfortable with EC2 servers, VI, cat, more etc.
Regulated industry experience – Financial Services, SOX, MRA, audit, NPPI/PII handling
Generative AI – Comfortable with Scripting and prompting. Understand when scripting is going to be more efficient and able to generate good scripts using well defined prompts and skill files.
Good Team and Communication Skills – Being Comfortable and willing to share your knowledge as well as learn from others. Coordinates well with other team members, project managers, and other teams. The World is Accelerating and we are all learning together to navigate the new changes to build Best in Class Platforms.
Salary Range:
$120,000 - $202,500 Annual
The range quoted above applies to the role in the primary location specified. If the candidate would ultimately work outside of the primary location above, the applicable range could differ.
Employees are eligible to