Role Overview
Key Responsibilities
Architecture & Solution Design
Cloud Architecture on AWS
Application Performance & Scalability
Security & Compliance
Technical Leadership
DevOps, Observability & Delivery
Required Skills & Experience
Preferred Qualifications
Soft Skills
Job Duties:
Define and implement a cloud-native data architecture that supports analytics, real-time processing, and advanced AI/ML workloads.
Develop a comprehensive modernization roadmap, balancing speed, risk, cost, and business continuity.
Partner with data engineering, analytics, infrastructure, and data science teams to design scalable pipelines and governance frameworks.
Define data ingestion, transformation, and integration strategies across diverse data sources.
Optimize performance, scalability, and cost within Snowflake and Databricks environments.
Establish best practices for DevOps, CI/CD, and Infrastructure as Code (e.g., Terraform).
Provide technical leadership and mentorship to data engineers and architects.
Stay ahead of evolving AI, ML, and LLM capabilities and ensure the platform is future-ready for those workloads.
Qualifications:
15 + years of experience in data engineering, data architecture, or platform engineering roles.
Proven track record migrating large-scale on-prem data warehouses (Oracle, Teradata, etc.) to Snowflake.
Demonstrated experience migrating Hadoop environments to Databricks, including Spark, Hive, and HDFS.
Deep expertise in SQL, Spark, Python, and distributed data processing frameworks.
Strong understanding of cloud architectures (AWS, Azure, or GCP) including storage, compute, networking, and security.
Experience with data governance, lineage, and cataloging tools (Purview, Collibra, Alation, etc.).
Familiarity with real-time data streaming (Kafka, Kinesis, or Pub/Sub) and orchestration frameworks (Airflow, dbt).
Proven leadership in cross-functional collaboration and migration program management.
Excellent communication skills—able to articulate technical concepts to both technical and non-technical stakeholders.
Nice to Have:
Certifications in Snowflake, Databricks, or major cloud providers (AWS, Azure, GCP).
Experience with machine learning and AI pipelines on Databricks or similar platforms.
Prior experience in data platform modernization or transformation programs at scale (multi-petabyte preferred).
Knowledge of modern data mesh, lakehouse, and data product principles.
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