Cloud Data & AI Architect

Industry:

Experience:

The Role

Cloud Data & AI Architect

This is a critical role where you'll collaborate with data scientists, engineers, and business leaders to turn complex data challenges into powerful, actionable insights. If you are passionate about building modern data platforms and have deep expertise across the entire data lifecycle, we want to talk to you!

What You'll Do

• Architect & Design:

  Design, build, and oversee our cloud-native data architecture, ensuring it is scalable, secure, and cost-effective.

• Platform Leadership:

  Lead the technical strategy and implementation on modern data platforms like

  Snowflake

  ,

  Google BigQuery

  ,

  Amazon Redshift

  ,

  Azure Synapse

  , and

  Databricks

  .

• End-to-End Data Lifecycle Management:

  Engineer solutions for the complete data journey, including data ingestion, ETL/ELT processing, storage, transformation, governance, and consumption for analytics and AI/ML models.

• AI/ML Integration:

  Design infrastructure to support the deployment and scaling of machine learning models, working closely with our data science team to enable MLOps best practices.

• Stakeholder Collaboration:

  Act as the bridge between technical teams and business stakeholders, translating business requirements into technical solutions and data models.

• Innovation & Strategy:

  Stay ahead of industry trends, evaluate new technologies, and drive the continuous improvement of our data and AI capabilities.

What You'll Bring

• Proven Experience:

  Demonstrated experience in a role like Data Architect, Solutions Architect, or Cloud Engineer, with a strong focus on data-intensive systems.

• Cloud Expertise:

  Deep, hands-on experience with at least one major cloud provider (

  AWS, GCP, or Azure

  ).

• Modern Data Platform Mastery:

  Extensive, practical experience architecting and building solutions using platforms such as

  Snowflake

  ,

  BigQuery

  ,

  Redshift

  ,

  Synapse

  , and/or

  Databricks

  . You should be able to articulate the trade-offs between them.

• Data Lifecycle Knowledge:

  Solid understanding of the entire data lifecycle, including data modeling, data warehousing, data lakes, and ETL/ELT development.

• Technical Skills:

  Proficiency with SQL and at least one programming language (e.g.,

  Python

  ). Experience with infrastructure-as-code (e.g., Terraform) is a plus.

• Excellent Communicator:

  Ability to articulate complex technical concepts clearly to both technical and non-technical audiences.

Preferred Qualifications

• International Client Experience:

  Proven experience working with international customers, especially in the

  US and European markets

  .

• IT Services Background:

  Previous experience at a Tier 1 or Tier 2 Indian IT services company is a strong plus.

• Certifications:

  Relevant cloud certifications (

  AWS, GCP, Azure

  ) and data transformation certifications (

  dbt

  , dataform) are highly preferred.

• AI Agent Systems:

  Experience with multi-agent AI systems and frameworks like

  Crew AI

  or

  Google Agent Space

  is a significant advantage.

• Streaming & ML Ops:

  Experience with data streaming technologies (e.g., Kafka, Kinesis) and ML Ops frameworks (e.g., Kubeflow, ML flow).