Senior Data Platform Architect for AI/ML Enablement

ProArch

United States

Remote

USD 140,000 - 220,000

Full time

2 days ago

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Job summary

ProArch is seeking a data platform architect to lead the evolution of our enterprise data platform strategy across cloud environments. You will establish standards, govern tool selections, and guide teams to implement scalable, secure, and compliant data pipelines supporting AI/ML workloads.

The role requires deep SQL/Python fluency, hands-on architectural leadership, and experience with Snowflake, Databricks, and ETL/ELT orchestration. Strong communication and agile collaboration are essential.

Qualifications

Bachelor’s degree in Information Technology, Data Science, Computer Science, or related field; or an equivalent combination of education and experience.
7+ years in database engineering with 3+ years in database architecture design and support.

Responsibilities

Lead the definition and evolution of the enterprise data platform strategy by developing, maintaining, and continuously evolving architecture standards, reference architectures, and design patterns aligned with business priorities, analytical requirements, and AI/ML enablement across data and ETL/ELT platforms.
Develop, maintain, and steward enterprise architecture artifacts and documentation—including current and target state designs, diagrams, standards, and decision records—ensuring accuracy, consistency, and accessibility within the architecture repository.
Evaluate, recommend, and govern the selection of data platforms and ETL/ELT tools, balancing scalability, performance, security, cost, and operational maturity while reducing unnecessary platform sprawl.
Review and guide data platform and pipeline designs to ensure consistency with enterprise standards, scalability requirements, and architectural best practices.
Provide technical leadership and guidance to data engineering teams, analytics teams, platform product owners to translate architecture standards into implementable solutions and influence technical direction across the organization.
Define architectural guardrails that ensure data platforms can scale reliably, meet performance expectations, and support high availability and disaster recovery requirements.
Establish architectural standards for platform operations, environment management, reliability, observability, deployment automation, and lifecycle management to ensure data platforms are production-ready and supportable.
Ensure data platform architectures incorporate security controls, data privacy requirements, and regulatory compliance (e.g., access controls, encryption, data lineage, and retention) by default.
Define and standardize ETL/ELT and data integration patterns, including batch, streaming, and event driven approaches, to support analytics, operational reporting, and downstream consumption.
Participate in enterprise architecture governance forums, providing clear architectural guidance and decision support for data platform and integration initiatives.
Incorporate financial discipline into platform architecture by guiding cost efficient design, platform right sizing, and lifecycle management in partnership with FinOps and platform teams.
Continuously assess emerging data platform and ETL technologies, recommending updates to standards and strategy as capabilities and business needs evolve.
Drive innovation in AI-powered data engineering workflows and automation.