Location: United Kingdom (Work from Office - Onsite Presence is required) 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 (e.g., 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. • 12+ years of experience in data architecture and data engineering with proven skills and leadership in large-scale cloud data programs. • 5+ 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 (e.g., 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 (e.g., 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.