Overview
Skills
Job Details
Job Title: Data Analytics Cloud Architect
Job Description
We are seeking an accomplished Data Analytics Cloud Architect with a proven track record in designing and implementing large-scale, cloud-native data platforms and analytics solutions. The ideal candidate will combine deep hands-on technical expertise with strategic architectural vision to drive enterprise data modernization initiatives.
Key Responsibilities
Architect and implement end-to-end data and analytics ecosystems leveraging AWS, Databricks, and Snowflake.
Design data lakehouse architectures that ensure scalability, resilience, and performance across diverse data domains.
Define and enforce data governance, security, and compliance frameworks aligned with enterprise and regulatory standards.
Build and optimize ETL/ELT pipelines, data models, and real-time data streaming solutions.
Lead performance tuning, cost optimization, and automation of data engineering workloads.
Partner with cross-functional teams to establish best practices, reusable frameworks, and DevOps-driven CI/CD for data pipelines.
Serve as a trusted advisor to senior stakeholders on data strategy, modernization roadmaps, and technology adoption.
Required Qualifications
10+ years of experience in enterprise data architecture, with a focus on cloud-based analytics platforms.
Hands-on expertise with AWS services: EMR, S3, Glue, Lambda, Redshift, Lake Formation.
Strong practical experience with Databricks including cluster management, Delta Lake, Unity Catalog, and performance optimization.
Advanced knowledge of Snowflake architecture, query optimization, and secure data sharing.
In-depth understanding of data lake and lakehouse design patterns, data modeling (dimensional, Data Vault, ER), and real-time data processing.
Strong grasp of ETL/ELT frameworks, data observability, and metadata management.
Excellent communication, leadership, and stakeholder management skills.
Preferred Qualifications
AWS Certified Solutions Architect Professional (or equivalent certification).
Experience in regulated domains (finance, healthcare, manufacturing).
Familiarity with DevOps and CI/CD practices for data workloads.
Exposure to FinOps, cost governance, and data cataloging tools such as Collibra or Alation.
Candidate Screening Questions
Below are structured screening questions designed to validate both hands-on expertise and architectural depth for the Data Analytics Cloud Architect role.
1. Cloud & Lakehouse Architecture
Question:
Can you describe a Lakehouse architecture you designed or implemented using AWS (S3, EMR, Glue, Lake Formation, Redshift) and Databricks? How did you ensure scalability, reliability, and data consistency?
Evaluation Focus: End-to-end design ownership, architectural reasoning, and mastery of lakehouse principles.
2. AWS Well-Architected Framework
Question:
How do you apply the AWS Well-Architected Framework pillars Operational Excellence, Security, Reliability, Performance Efficiency, and Cost Optimization when designing analytics solutions? Please share a recent example.
Evaluation Focus: Architectural maturity and AWS best-practice alignment.
3. Databricks Platform Experience
Question:
How have you leveraged Databricks for large-scale data engineering or analytics workloads? Describe how you configured clusters, Delta tables, Unity Catalog, and managed performance or cost.
Evaluation Focus: Deep Databricks expertise and practical optimization experience.
4. Databricks Governance & Security
Question:
How have you implemented governance, role-based access, and lineage in Databricks? Have you utilized Unity Catalog or similar tools for compliance enforcement?
Evaluation Focus: Governance and data security implementation.
5. Reusable Frameworks & Automation
Question:
Have you designed reusable frameworks or accelerators for ingestion, transformation, or ML/AI workloads? How did you automate provisioning, monitoring, and quality checks?
Evaluation Focus: Engineering scalability, automation mindset, and reusability.
6. Cloud Cost Management (FinOps)
Question:
What approaches or tools have you used to monitor and optimize AWS or Databricks costs? How do you balance cost efficiency with performance requirements?
Evaluation Focus: Financial discipline and FinOps awareness.
7. Data Quality & Observability
Question:
What tools or frameworks have you used for data quality validation, observability, and pipeline monitoring? Have you implemented lineage tracking or alerting?
Evaluation Focus: Data reliability, observability, and proactive monitoring.
8. Real-Time & Streaming Data
Question:
Describe a project where you implemented real-time streaming data using Kinesis, Kafka, or Spark Structured Streaming. What business impact did it deliver?
Evaluation Focus: Event-driven architecture and real-time integration experience.
9. Data Modeling & Warehousing
Question:
What is your experience with dimensional modeling, Data Vault, or ER modeling? Have you used tools like Erwin for enterprise warehouse design?
Evaluation Focus: Data modeling acumen and warehouse architecture proficiency.
10. Governance & Best Practices
Question:
What governance frameworks or best practices have you implemented for pipeline management, access control, and cross-cloud compliance?
Evaluation Focus: Enterprise governance strategy and architectural foresight.