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.
