Data Architect

About us

Diacto Technologies is a Data & AI consultancy that helps enterprises turn fragmented data into trusted, AI-ready platforms. Headquartered in Pune, India, with US offices in Austin, TX and Salt Lake City, UT, we deliver for enterprise clients across the US, ANZ, India and APAC.

We are certified partners of Snowflake, Databricks, ClickHouse and Domo, and an OpenAI partner. Our teams are AI-native: we use modern AI tooling in how we design, build and deliver, not just in what we ship to clients.

We value ownership, clear thinking and outcomes customers can measure. You will work alongside architects and engineers who care about doing things the right way, and you will have room to grow as we do.

About the Role

We are hiring a (REMOTE) Data Architect to design and lead modern data platforms for our enterprise clients, with Snowflake as the primary data warehouse running alongside Google Cloud Platform services. You will own the architecture end to end: ingestion, orchestration with Airflow, transformation with dbt, infrastructure as code with Terraform, and AI-ready data foundations on Vertex AI. You will work directly with client stakeholders as well as our engineering teams.

Responsibilities

• GCP data architecture: Design and govern scalable, cost-efficient data platforms on Google Cloud using services such as Cloud Storage, Cloud Run, Pub/Sub, Dataflow and Dataproc.
• Snowflake platform design: Architect Snowflake as the primary data warehouse, including warehouse sizing, role-based access, data sharing, and cost and performance tuning. Integrate Snowflake cleanly with GCP sources and services.
• Orchestration with Airflow: Design reliable pipeline orchestration on Apache Airflow (including Cloud Composer), with clear dependency management, retries, alerting and SLAs.
• Transformation with dbt: Set the standards for dbt projects: layered modeling, testing, documentation, macros and environment promotion.
• Infrastructure as code: Provision and manage data infrastructure with Terraform, keeping environments reproducible, versioned and auditable.
• Version control and CI/CD: Establish Git-based workflows, branching strategies, code reviews and CI/CD pipelines for data code and infrastructure.
• AI and agent enablement: Design data foundations for ML and generative AI use cases on Google Cloud Vertex AI and Google's agent platform, including feature pipelines, retrieval-ready data and model integration.
• Data modeling: Build conceptual, logical and physical data models (dimensional, Data Vault or similar) that fit each client's analytics and operational needs.
• Data governance: Define and enforce standards for data quality, security, privacy, lineage and metadata management.
• Performance and reliability: Monitor, tune and right-size data systems for performance, availability and cloud spend.
• Client and team leadership: Translate business requirements into architecture, contribute to solution design and estimates in pre-sales, and guide data engineers through delivery.
• Continuous improvement: Track new developments in data and AI platforms and bring practical improvements to our delivery playbooks.

Qualifications

• 5+ 10 years in data engineering and data architecture, with at least 3 years designing cloud data platforms.
• Google Cloud Platform: Deep, hands-on knowledge of GCP data services, including Cloud Storage, Cloud Run, Pub/Sub, Dataflow, Dataproc and Cloud Composer.
• Snowflake: Proven experience designing and running production Snowflake environments.
• Apache Airflow: Strong experience building and operating production DAGs.
• dbt: Hands-on expertise in dbt modeling, testing and deployment practices.
• Terraform: Experience managing cloud and data infrastructure as code.
• Git: Fluency with Git workflows, code review and CI/CD for data projects.
• Vertex AI: Working knowledge of Vertex AI and Googl's agent platform for ML and generative AI workloads.
• Programming: Advanced SQL and strong Python.
• Data modeling and governance: Expertise in modeling techniques and a solid grasp of data quality, security, compliance and metadata management.
• Communication: Clear written and verbal communication, and comfort presenting architecture to both technical and business audiences.
• Bachelor's degree in Computer Science, Computer Engineering, Data or a related field, or equivalent work experience.

Nice to Have

• Google Cloud Professional Data Engineer or Professional Cloud Architect certification.
• SnowPro Core or SnowPro Advanced: Architect certification.
• Hands-on experience with Spark or other large-scale processing frameworks.
• Experience in a consulting or client-facing delivery role, including solution design and effort estimation.
• Exposure to other platforms in our ecosystem, such as Databricks or ClickHouse.