Solutions Architect - Modern Data Management Platforms

Location: Candidate must be based in the San Francisco Bay Area. Our Company e're Hitachi Vantara, the data foundation trusted by the world's innovators. Our resilient, high-performance data infrastructure means that customers - from banks to theme parks - can focus on achieving the incredible with data. If you've seen the Las Vegas Sphere, you've seen just one example of how we empower businesses to automate, optimize, innovate - and wow their customers. Right now, we're laying the foundation for our next wave of growth. We're looking for people who love being part of a diverse, global team - and who get excited about making a real-world impact with data We are seeking a senior Solutions Architect with 10+ years of experience designing, validating, and delivering enterprise data management solutions. This role will own the technical architecture and solution development for modern data platforms that span open table formats, object storage, lakehouse architectures, data governance, compliance-aware architecture, streaming data pipelines, metadata services, federated query environments, and AI-ready data foundations. The ideal candidate is a hands-on architect who can move fluidly between strategy, architecture, validation, and customer-facing guidance. They should be comfortable building reference architectures, proving technical patterns in lab environments, partnering with engineering and product teams, and helping customers understand how Hitachi platforms can support governed, compliant, scalable, high-performance data management initiativ es. Core Mission Own the technical validation, architecture, and solution development of modern data management platforms for Hitachi environments, with emphasis on open, governed, compliant, interoperable, and AI-ready data architectures. Open table formats, especially Apache Iceberg File and object storage architectures, including S3-compatible platforms Data lake and data lakehouse architectures Data governance, compliance, metadata management, lineage, and policy enforcement Compliance-aware data architecture, including privacy, retention, classification, auditability, and regulatory controls Data preparation, ETL, ELT, and batch processing patternsStreaming data pipelines and real-time data movementAI-ready data foundations for analytics, RAG, and generative AI use cases Metadata catalogs, data catalogs, and catalog interoperability Federated query, data virtualization, and multi-engine query environments Customer-facing reference architectures and solution guidance for Hitachi platforms What You Will be Doing Design and validate end-to-end data management architectures that combine object storage, open table formats, query engines, governance services, compliance controls, and data pipeline technologies. Develop reference architectures for data lakehouse platforms using technologies such as Apache Iceberg, Parquet, Spark, Kafka, Flink, Airflow, and S3-compatible storage. Build and document technical patterns for data preparation, ETL, ELT, batch processing, and streaming pipelines that support production-grade customer deployments. Define architectural guidance for metadata catalogs, data lineage, data quality, data classification, PII discovery, policy enforcement, auditability, retention, and compliance operating models. Ensure solution architectures account for compliance requirements early in the design process, including privacy, security, regulatory alignment, data residency, retention, classification, and access governance. Validate interoperability between Hitachi platforms and modern data ecosystem components, including catalog services, query engines, data engineering tools, and AI data services. Create customer-facing solution briefs, design guides, technical white papers, demos, and best-practice documentation. Partner with product management, engineering, field teams, and strategic customers to translate business and technical requirements into repeatable solution architectures. Support proof-of-concept activities, technical workshops, and executive-level solution discussions with customers and partners. Evaluate emerging technologies in open table formats, data lakehouse architectures, federated query, AI data management, governance frameworks, and compliance-aware data management practices. Serve as a subject matter expert for modern data management, helping position Hitachi platforms as trusted foundations for governed, compliant analytics and AI workloads. What You Will Bring to the team 10+ years of experience in solutions architecture, data architecture, data engineering, enterprise storage, analytics platforms, or related technical roles. Proven experience designing production data platforms, not simply consuming data services or operating prebuilt environments. Demonstrated ability to architect data platforms with compliance in mind, including privacy, retention, audit, classification, policy enforcement, and regulatory considerations. Strong ability to translate complex technical concepts into clear customer-facing guidance, reference architectures, and executive-ready solution narratives. Hands-on experience validating architectures in lab, proof-of-concept, or customer deployment environments. Ability to work across engineering, product, field, partner, and customer teams to drive solution development from concept through validation and enablement. Must-Have Skills Modern Data Platforms Deep understanding of data lakes, data lakehouse architectures, object storage architectures, S3-compatible storage patterns, and how these designs support governance, security, compliance, and auditability. Strong knowledge of Apache Iceberg, Parquet, and open table format concepts; Delta Lake experience is a plus. Ability to explain why open table formats matter, including metadata management, schema evolution, transactional consistency, time travel, multi-engine access, and interoperability. Working knowledge of metadata catalogs and how they support table discovery, governance, policy enforcement, compliance workflows, auditability, and query engine integration. Data Engineering Hands-on experience building ETL, ELT, batch processing, and data preparation archite ctures.Production experience with data engineering tools such as Apache Spark, Kafka, Flink, Airflow, and Pentaho PDI Ability to design data pipelines that integrate streaming, batch, transformation, metadata, storage, governance, and compliance controls Experience with architectures that combine Kafka, Spark, PDI, Iceberg, and object storage for governed, compliant data preparation and analytics Data Governance Strong experience with cataloging, metadata management, data lineage, data quality, data classification, PII discovery, policy enforcement, auditability, retention, and compliance support Familiarity with governance platforms such as Collibra, Alation, Microsoft Purview, Informatica, DataHub, or OpenMetadata Understanding of governance and compliance operating models, including stewardship, ownership, policy definition, classification, access controls, audit readiness, retention policies, and compliance worflows Ability to connect governance and compliance practices to technical architecture decisions across storage, catalog, query, pipeline, and AI layers AI Data Foundation Experience Experience supporting data foundations for AI, generative AI, analytics, and RAG pipelines Understanding of vector embeddings, semantic metadata, metadata enrichment, AI governance, data preparation for AI, and vector database concepts Ability to explain why AI initiatives require governed, compliant, trusted, explainable, and well-documented data Knowledge of how lineage, quality, classification, compliance controls, and metadata improve trust, explainability, and operational readiness for AI workloads Lakehouse Query Engines and Federation Experience with at least one modern lakehouse or federated query engine, such as Trino, Presto, Starburst, Athena, Snowflake, Databricks SQL, Dremio, Denodo, or Zetaris-like federation platforms Understanding of data federation, data virtualization, predicate pushdown, query optimization, catalog integration, governance-aware query access, and compliance-aware data access controls Ability to design architectures where multiple engines can safely access shared lakehouse data through governed, compliant metadata and catalog services As required by the equal pay and transparency acts, the expected base salary for this position is: $135K to $150k. Expected on-target earnings: $157K to $173K. The expected pay is determined based on a variety of factors including, but not limited to, depth of experience in the practice area. Employees are eligible to participate in Hitachi Vantara’s bonus/variable/commission pay programs, where applicable, and are subject to the program’s conditions and restrictions. About Us We're a global team of innovators. Together, we harness engineering excellence and passion for insight to co-create meaningful solutions to complex challenges. We turn organizations into data-driven leaders that can make a positive impact on their industries and society. If you believe that innovation can inspire the future, this is the place to fulfil your purpose and achieve your p