UST
Website:
ussmokeless.com
Company:
https://www.linkedin.com/company/ustglobal
Seniority: Not Applicable
Industries: IT Services and IT Consulting
Job details:
Role Description
Principal Architect — Data Platform & Lakehouse Who We Look For We are looking for a Principal Architect — Data Platform & Lakehouse to own the technical direction of enterprise data architecture, Apache Iceberg-based lakehouse capabilities, metadata, analytical infrastructure, and document management across the UST FinX platform. This is a senior, hands-on architecture and engineering role — not advisory or governance-only. Your influence comes from designing scalable platforms, building reusable frameworks, defining strong architecture patterns, writing production-grade code, and guiding teams to deliver data infrastructure that is reliable, secure, governed, observable, and usable. You understand that a lakehouse is more than a storage bucket with query access: it requires table-format discipline, catalog architecture, data modeling, schema and partition evolution, metadata management, governance, access control, lifecycle management, performance tuning, and clear ownership of data products. You stay close to implementation — writing Java, building frameworks, reviewing data contracts, debugging pipelines, and evolving catalog and metadata capabilities. In an AI-assisted development environment, you apply strong engineering judgment to validate, refine, and sometimes reject generated output, ensuring components meet high standards for correctness, resilience, security, scalability, and maintainability. We value a collaborative, merit-driven culture and expect senior architects to constructively challenge weak designs, simplify complexity, and converge teams on strong reusable patterns. What You Will Do Define and evolve the data platform and lakehouse architecture strategy for UST FinX. Own the technical architecture for Apache Iceberg-based lakehouse capabilities — table design, catalog integration, schema and partition evolution, metadata, compaction, snapshots, retention, performance, and governance. Build reusable patterns, frameworks, and reference implementations for ingestion, storage, metadata, data access, document management, analytics, and table lifecycle management. Provide technical ownership for enterprise data architecture across platform, integration, and orchestration services, document repositories, and analytical platforms. Guide teams on Iceberg table design, catalog selection, partitioning, query performance, data lifecycle, and multi-tenant isolation. Define standards for data contracts, metadata, lineage, governance, retention, access control, and data quality. Work hands-on with Java-based platform services, ingestion frameworks, metadata services, and storage abstractions. Partner with backend, platform, data, security, DevOps, and QA engineers plus product and delivery teams to ship reliable, governed, production-ready capabilities. Mentor engineers through design and code reviews, pairing, and incident analysis. Ensure data systems are secure, scalable, resilient, observable, cost-aware, and suitable for regulated banking environments. Reporting Line and Role Context This role reports directly to the CTO of UST FinX. You will operate as one of the CTO’s senior technical lieutenants — creating engineering leverage across the organization while remaining deeply hands-on. You will influence multiple pods, establish reusable standards, mentor technical leaders, and turn technical direction into production-quality execution. This is not a passive advisory or traditional people-management role; your authority comes from technical credibility, judgment, and execution. You will work closely with the CTO to define standards, identify engineering gaps, raise delivery quality, and ensure architecture is reflected in real code and operational behavior — a true force multiplier for the engineering organization. Skills and Experience We Are Looking For Minimum 8 years (ideally 10–15) in software, data, platform engineering, or architecture, with meaningful ownership of production-scale data platforms. Strong experience designing and implementing large-scale lakehouse platforms in production. Deep experience with Apache Iceberg — snapshots, schema and partition evolution, hidden partitioning, compaction, time travel, table maintenance, and query performance. Strong knowledge of lakehouse catalog and metadata layers (AWS Glue Data Catalog, Hive Metastore, Nessie, REST catalogs, or equivalent). Experience with object-storage-based platforms — storage layout, lifecycle policies, encryption, access control, cost management, and raw/curated/governed/analytical zone separation. Strong metadata management skills — cataloging, lineage, schema management, discoverability, governance, and auditability. Experience building ingestion pipelines across batch, streaming, event-driven, CDC, file-, and API-based patterns. Strong Java background — designing, implementing, reviewing, and debugging production platform and infrastructure code. Understanding of multi-tenant architectures — tenant isolation, access boundaries, metadata separation, and operational controls. Understanding of data governance — ownership, classification, retention, access control, lineage, data quality, and regulatory expectations. Experience supporting analytics — data modeling, query tuning, semantic layers, and BI integration. Experience building reusable frameworks and shared libraries consumed by multiple teams. Strong communication and mentoring skills to raise data architecture maturity across globally distributed teams without relying on formal authority. Preferred Qualifications AWS data platforms (S3, Glue, Lake Formation, Athena, EMR, Redshift, Kinesis, MSK, Lambda, IAM, KMS). Spark, Flink, Trino, Presto, Dremio, Snowflake, Databricks, or equivalent processing and analytical technologies. Iceberg operational tooling — table optimization, snapshot expiration, orphan file removal, compaction, and catalog migration — across multiple query engines. Document management infrastructure — object-backed storage, metadata indexing, lifecycle, retention, search, and workflow/case-management integration. Event-driven ingestion — Kafka or Redpanda, schema registries, event contracts, CDC, and transactional outbox. Data platforms for financial services, banking, payments, lending, compliance, or regulated enterprise environments. API-first, data-product-oriented architectures with governed access APIs. Observability for data platforms — pipeline metrics, data quality checks, freshness, ing, and dashboards. CI/CD, automated testing, data contract testing, and production-readiness practices. Security patterns — IAM, encryption, secrets management, network isolation, and row-/object-level access control. Experience living with the long-term consequences of data architecture decisions — supporting, scaling, migrating, and evolving critical production platforms.
Skills
Data Lakes, Data Modeling, Data Validation, Data Governance, Databricks, AWS Lake Formation, Snowflake, Apache Hive, AWS, SaaS, Enterprise Content Management
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