As a Google and Snowflake Data Cloud Architect, you will design and lead modern data and AI platforms on Google Cloud and Snowflake, with selective use of multicloud scenarios.
You will work with different clients to understand the business problems, define roadmaps, shape architectures, and take analytics, ML, GenAI and agentic use cases from idea to production, combining deep technical skills with strong presales experience.
What You Will Do:
- Architect end to end data and AI platforms using Google Cloud (BigQuery, Dataflow, Dataproc, Pub/Sub, AlloyDB, Vertex AI, Cloud Composer) and Snowflake.
- Design data lake and warehouse models, ingestion and ELT pipelines for batch, streaming and near real time needs using tools such as dbt, Airflow or Cloud Composer.
- Enable LLM, RAG and agentic AI scenarios using solid data and vector foundations, integrating Vertex AI and Snowflake capabilities where appropriate.
- Lead client workshops, solution design, sizing, effort estimation and proposal or SOW creation as part of presales cycles.
- Guide delivery teams through design reviews, code and pipeline best practices, CI/CD for data and ML and mentor engineers and junior architects.
What You Need to Succeed:
- 8+ years in data architecture or data engineering with strong, recent hands on experience on Google Cloud as a must have.
- Proven presales experience with clients, including workshops, solution scoping, estimates and proposal content.
- Deep skills in Google Cloud data services (BigQuery, Dataflow, Dataproc, Pub/Sub, AlloyDB, Cloud Composer, Looker) and practical Snowflake expertise (roles, warehouses, performance and cost tuning).
- Hands on work with ML or GenAI (Vertex AI or similar), strong SQL and Python and familiarity with LLM, vector and agentic patterns.
- Knowledge of AWS data services (S3, Glue, Redshift, Lambda) and experience with data governance, security and Responsible AI in enterprise environments.
Education and Certifications:
- Bachelor's degree in computer science, information technology, engineering or a related field, or equivalent practical experience commonly expected for cloud and data architects.
- Certifications such as Google Cloud Professional Cloud Architect, Google Cloud Professional Data Engineer, Snowflake SnowPro Advanced Architect or AWS Solutions Architect Professional are strongly preferred.
Nice to Have:
- Experience with BI tools such as Power BI, Tableau, Looker or Looker Studio for analytics and dashboarding.
- Experience with infrastructure as code and DevOps for data, for example Terraform, Cloud Deployment Manager, Git based workflows and CI/CD for pipelines.
- Consulting background and contributions to data or AI communities through blogs, talks or meetups.
