Cloud Data Transformation Practice Lead

2 days ago

bangalore, India Amdocs Full time

Role Summary As the Cloud Data Transformation Practice Lead, you will architect and deliver the modernization of enterprise data platforms—migrating 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.

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