Solutions Architect - Modern Data Management 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.

Hands-on experience building ETL, ELT, batch processing, and data preparation architectures.

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.

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 workflows.

Ability to connect governance and compliance practices to technical architecture decisions across storage, catalog, query, pipeline, and AI layers.

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.

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.

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