Cloud Data Transformation Practice Lead

Role Summary

As the Cloud Data Transformation Practice Lead, you will architect and deliver the modernization of enterprise data platformsmigrating on-premises, structured, and unstructured databases to scalable, cloud-native solutions on AWS, Azure, GCP, Snowflake, and Databricks. You will define frameworks and reusable assets for efficient, secure, and analytics-ready data transformation, enabling advanced analytics, AI/ML, and business value acceleration.

Key Responsibilities

• Strategic Leadership: Define and execute the data transformation roadmap; drive business growth and practice differentiation.
• Reusable Assets & IP: Build and maintain reusable tools, accelerators, frameworks, and intellectual property for data migration, ETL/ELT, data modeling, and cloud-native pipelines.
• AI/ML & DataOps: Embed AI/ML capabilities into data solutions; champion DataOps practices (CI/CD, automated testing, monitoring, observability) for data workloads.
• Cloud Platform Expertise: Lead migrations to AWS, Azure, GCP, Snowflake, and Databricks; ensure best practices in data security, privacy, and compliance.
• Executive & Customer Engagement: Present technical solutions, transformation roadmaps, and business value to executives and customers; deliver technical briefings and workshops.
• RFPs, Proposals & SOWs: Actively participate in RFP responses, proposal development, and SOW creation for data transformation opportunities.
• Collaboration: Work closely with delivery, pre-sales/GTM and R&D teams to deliver integrated solutions.
• Mentorship: Lead and mentor teams, fostering a culture of innovation and continuous improvement.

Technical Skills Required

• 15+ years in data engineering, with deep expertise in AWS, Azure , GCP, Snowflake, and Databricks.
• Proven experience migrating on-premises structured/unstructured databases to cloud.
• Mastery of ETL/ELT, data modeling, and building scalable data pipelines (batch, streaming, real-time).
• Strong programming skills (Python AND Spark ) and experience with cloud-native data services.
• Demonstrated experience with DataOps: CI/CD, data quality, lineage, and observability tools.
• Hands-on experience embedding AI/ML into data platforms and enabling advanced analytics.
• Data security, IAM, encryption, and regulatory compliance expertise.
• Leadership and consulting experience, with strong communication and executive presentation skills.
• hands of experience on Hyperscalers (Azure, AWS, GCP) Data services.

Data Monitoring & Observability Tools/Tech Stack

• Experience with monitoring and observability tools such as Prometheus, Grafana, ELK/EFK Stack, Datadog, CloudWatch (AWS), Azure Monitor, Google Cloud Operations Suite, OpenTelemetry, Great Expectations, Monte Carlo, and OpenLineage.

Nice-to-Have

• Certifications in AWS, Azure, GCP, Snowflake, or Databricks.
• Experience with open-source data engineering tools.
• Background in AI/ML, app modernization, or cloud security.