jazzhr
Enterprise ServiceNow Architect
Bright Vision Technologies
Remote, USA
Full-time
Senior Level
Remote
USD 100000-150000 yearly
Discovered 1 weeks ago
Free
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- Define the target-state architecture of the enterprise data platform, including ingestion, storage, processing, and consumption layers.
- Establish standards for data modeling, schema evolution, partitioning, file formats, and storage organization.
- Architect lakehouse, warehouse, and streaming patterns leveraging technologies such as Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta Lake, or Hudi.
- Design end-to-end data pipelines that balance latency, cost, reliability, and maintainability across batch and streaming workloads.
- Lead the integration of governance, lineage, and catalog tools such as Collibra, Alation, Atlan, Unity Catalog, or DataHub.
- Define security architecture including row- and column-level controls, masking, encryption, and identity-aware access patterns.
- Partner with ML, BI, and product teams to ensure platform capabilities align with downstream consumption needs.
- Establish data contract and data product principles to drive ownership, quality, and decoupling between producers and consumers.
- Lead architecture reviews and provide guidance on pipeline and warehouse design proposals across teams.
- Drive cost optimization and capacity planning across the data platform estate.
- Design disaster recovery, multi-region, and high-availability strategies for critical data assets.
- Mentor data engineers and architects on platform standards and emerging best practices.
- Produce architecture artifacts including context diagrams, decision records, and reference patterns.
- Stay current with data platform research, vendor offerings, and open-source ecosystem developments.
- Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field.
- Eight or more years of experience in data engineering, with significant time in architecture roles.
- Deep expertise across at least two major data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
- Strong understanding of lakehouse architectures, modern table formats, and streaming systems.
- Hands-on experience with Spark, Flink, or Kafka at production scale.
- Strong data modeling expertise across dimensional, normalized, and data-vault patterns.
- Experience implementing governance, lineage, and catalog capabilities.
- Solid grasp of cloud platforms, networking, identity, and cost optimization.
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