Sr Cloud Data Architect

Key Responsibilities

Provide technical leadership and strategic direction for enterprise-scale data migration and modernization initiatives. Architect end-to-end data platforms using Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Define and implement real-time and batch processing pipelines for complex use cases involving streaming analytics, ML feature engineering, and automation. Act as a trusted advisor to senior technical and business stakeholders across industries (, telecom, retail, financial services). Drive data quality, governance, lineage, and security standards across enterprise data pipelines. Mentor engineering teams and lead best practice adoption across data architecture, orchestration, and DevOps tooling. Participate in technical workshops, executive briefings, and architecture reviews to evangelize GCP data capabilities.

Required Qualifications Bachelor's or Master's degree in Computer Science, Data Engineering, or related technical field. + years of experience in data architecture and data engineering with proven skills and leadership in large-scale cloud data programs. + years of hands-on experience as a Data Engineer, with at least + years specifically working with Google Cloud Platform (GCP) data services. Strong proficiency in SQL and experience with schema design and query optimization for large datasets. Expertise in BigQuery, including advanced SQL, partitioning, clustering, and performance tuning. Hands-on experience with at least one of the following GCP data processing services: Dataflow (Apache Beam), Dataproc (Apache Spark/Hadoop), or Composer (Apache Airflow). Proficiency in at least one scripting/programming language (, Python, Java, Scala) for data manipulation and pipeline development. Understanding of data warehousing and data lake concepts and best practices. Experience with version control systems (, Git). + years of advanced expertise in Google Cloud data services: Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Hands-on experience with orchestration tools like Apache Airflow or Cloud Composer. Hands-on experience with one or more of the following GCP data processing services: Dataflow (Apache Beam), Dataproc (Apache Spark/Hadoop), or Composer (Apache Airflow). Proficiency in at least one scripting/programming language (, Python, Java, Scala) for data manipulation and pipeline development. Scala is mandated in some cases. Deep understanding of data lakehouse design, event-driven architecture, and hybrid cloud data strategies. Strong proficiency in SQL and experience with schema design and query optimization for large datasets. Expertise in BigQuery, including advanced SQL, partitioning, clustering, and performance tuning. Experience with version control systems (, Git). Track record of success advising C-level executives and aligning technical solutions with business goals. Google Professional Data Engineer certification(Mandatory).Google Professional Cloud Architect certification or equivalent. Preferred Qualifications Experience with modernization of on-premise and mainframe data environments into cloud-native architectures. Knowledge of regulatory data requirements across financial, healthcare, and telecom sectors. Familiarity with IaC (Terraform), GitOps, and CI/CD for data pipeline deployment. Strong communication, stakeholder management, and mentoring skills.