Senior Data Platform Architect; Snowflake, Databricks, Platforms & Data Virtualization

Position: Senior Data Platform Architect (Snowflake, Databricks, Open Platforms & Data Virtualization), A[...]
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…