Tiger Analytics

Delivers Data and AI transformation

Databricks Architect

Data EngineeringConfirmed live in the last 24 hours

Full-timeRemote in USANo salary listedRemote

About the Job

Requirements

Strong experience in data architecture, specifically Databricks architecture.
Expertise in Databricks Lakehouse, including Delta Lake, Unity Catalog, and MLflow; AWS; Snowflake; and Apache Iceberg.
Strong MLOps and continuous integration/continuous delivery expertise.
Proficiency in Python or Scala with Spark for data governance, security, and enterprise data platform design.

Responsibilities

Architect and strategize Databricks on AWS and Snowflake integration for secure, scalable data and AI platforms.
Architect seamless Snowflake/Databricks data flow via Apache Iceberg, including machine-learning output.
Design a cost-managed, multi-region Databricks MLOps platform and data flows.
Implement Unity Catalog for fine-grained access control and secure team coexistence with Workspaces.
Develop Databricks-native MLOps, including environment parity, Git-driven continuous integration/continuous delivery, governed access, MLflow governance, standardized deployment, and monitoring.
Define enterprise-scalable AI governance for generative AI production deployment.
Design and implement best data practices.

Desired Qualifications

Databricks certifications.
Generative AI architecture experience.

Databricks Architect

Tiger Analytics Inc. · United States (Remote) · — · Posted 2025-06-24

Workplace: remote

Department: MLE

Description

Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world.

This role will be responsible for Databricks Architecture, Designing and implementing best data practices.

Requirements

Strong Experience in Data Architecture, specifically with Databricks Architecture.
Expert in Databricks Lakehouse (Delta Lake, Unity Catalog, MLflow), AWS, Snowflake, and Apache Iceberg.
Strong MLOps & CI/CD Expertise
Proficient in Python/Scala (Spark) for data governance, security, and enterprise data platform design.
Databricks certifications and GenAI architecture experience preferred.

Key Responsibilities:

Architect and strategize Databricks on AWS/Snowflake integration for secure, scalable data/AI platforms.
Architect seamless Snowflake/Databricks data flow via Apache Iceberg (including ML output).
Design cost-managed multi-region Databricks MLOps platform and data flows.
Implement Unity Catalog for fine-grained access control and secure team coexistence with Workspaces.
Develop Databricks-native MLOps (environment parity, Git-driven CI/CD, governed access, MLflow governance, standardized deployment, monitoring).
Define enterprise-scalable AI governance for GenAI production deployment.

Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.

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About the Company

Tiger Analytics

Delivers Data and AI transformation

Tiger Analytics helps large organizations plan and execute Data and AI–led transformations at scale. It works with clients to translate business needs into data and AI initiatives, using an approach that combines business value mapping, agile operating models, and a platforms-based architecture, backed by partnerships with major hyperscalers. The company designs and delivers end-to-end data and AI solutions—from strategy and data engineering to model development and deployment—so that AI can be applied to tough, real-world problems. Unlike firms that rely mainly on standard playbooks, Tiger Analytics builds custom approaches and accelerators to suit each client’s value chain and operating model. With a global team of 5000+ technologists and consultants, the goal is to enable measurable value and practical outcomes through data and AI programs that work at enterprise scale.

Company size: 1,001-5,000
Company stage: Not listed
Total funding: Not listed
Headquarters: San Jose, CA
Founded: 2010

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