BI / Data Architect

Siri InfoSolutions Inc; Seattle (WA); On-site; USD 120,000 - 180,000; Full time

Job summary

Siri InfoSolutions Inc is seeking a BI / Data Architect to design and implement scalable lakehouse architectures remotely across the United States. You will lead the definition of standards, governance, and roadmaps while building robust data layers and ingestion pipelines using Iceberg, Spark, Flink, Kafka, and dbt.

The role emphasizes cloud-based data platforms across AWS/Azure/Google Cloud, with strong focus on metadata, security, and performance optimization enabling AI-ready workloads.

Responsibilities

• Define enterprise lakehouse architecture, standards, governance, and implementation roadmap.
• Design and build Apache Iceberg-based Bronze, Silver, and Gold data layers.
• Develop ingestion frameworks for batch, streaming, CDC, APIs, and event-driven integrations.
• Build scalable Spark, Flink, Kafka, dbt, SQL, and Python pipelines.
• Implement metadata management, cataloguing, lineage, RBAC , encryption, branding? (typo?)
• Optimize partitioning, compaction, snapshots, metadata, and query performance.
• Enable AI-ready semantic layers, feature engineering, RAG, and GenAI workloads.
• Implement Infrastructure as Code, CI/CD, monitoring, and observability.

Tools

Apache Iceberg

Spark

Flink

Kafka

dbt

Trino

Snowflake

Databricks

AWS

Azure

Google Cloud

Python

Scala

SQL

Java

Kubernetes

Docker

Terraform

Airflow

Dagster

Prefect

Polaris

Unity Catalog

Glue Catalog

Apache Nessie

Job description

Job Title: BI / Data Architect

Job Location: Remote

Type of Requirement: Full Time Permanent

Responsibilities

Must Have Technical/Functional Skills

• Apache Iceberg, Spark, Flink, Kafka, dbt, Trino, Snowflake, Databricks
• AWS, Azure, or Google Cloud
• Python, Scala, SQL, Java
• Kubernetes, Docker, Terraform
• Airflow, Dagster, or Prefect
• Polaris, Unity Catalog, Glue Catalog, Apache Nessie

Roles & Responsibilities

• Define enterprise lakehouse architecture, standards, governance, and implementation roadmap.
• Design and build Apache Iceberg-based Bronze, Silver, and Gold data layers.
• Develop ingestion frameworks for batch, streaming, CDC, APIs, and event-driven integrations.
• Build scalable Spark, Flink, Kafka, dbt, SQL, and Python pipelines.
• Implement metadata management, cataloguing, lineage, RBAC , encryption, branding? (typo?)
• Optimize partitioning, compaction, snapshots, metadata, and query performance.
• Enable AI-ready semantic layers, feature engineering, RAG, and GenAI workloads.
• Implement Infrastructure as Code, CI/CD, monitoring, and observability.