Key Responsibilities: Design, develop, and maintain scalable data platforms and data pipelines on Google Cloud Platform (GCP). Architect and implement robust batch and real-time streaming data processing solutions. Build and optimize ETL/ELT pipelines for large-scale structured and unstructured data. Develop scalable data ingestion frameworks leveraging cloud-native technologies. Work with GCP services such as BigQuery, Dataflow, Pub/Sub, Cloud Storage, Dataproc, Composer, Cloud Functions, and BigLake. Lead technical design discussions and provide guidance on data architecture and best practices. Partner closely with business stakeholders, architects, analysts, and engineering teams to deliver high-quality data solutions. Ensure data quality, governance, security, scalability, and performance across the data ecosystem. Troubleshoot complex production issues and drive continuous improvement initiatives. Mentor junior and mid-level engineers and contribute to the overall engineering excellence of the team. Support CI/CD implementation, automation, and infrastructure-as-code initiatives. Required Qualifications: 8+ years of experience in Data Engineering, Data Warehousing, or Big Data technologies. Strong hands-on experience with Google Cloud Platform (GCP). Proven expertise in designing and implementing batch and streaming data processing solutions. Advanced proficiency in Python and SQL. Experience with distributed data processing frameworks and cloud-based analytics platforms. Strong understanding of data modeling, schema design, data quality, and performance optimization. Experience building enterprise-scale ETL/ELT pipelines and data integration frameworks. Familiarity with Agile delivery methodologies and DevOps practices. Strong problem-solving, analytical, and communication skills. Nice to Have Qualifications: Experience designing and integrating solutions using REST APIs. Prior experience in Wealth Management, Asset Management, Investment Banking, or Financial Services domains. Knowledge of market, portfolio, client, or investment data ecosystems. Experience with containerization technologies such as Docker and Kubernetes. GCP certifications such as Google Professional Data Engineer.