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About the role
Responsibilities: Design and architect scalable, secure, and high-performance data platforms on GCP
Lead the development of large-scale batch and streaming data pipelines using PySpark and Python Build and optimize orchestration frameworks using Apache Airflow/Cloud Composer Design enterprise-grade data models and transformation frameworks using dbt Implement scalable data warehouse and lakehouse solutions using BigQuery Define architecture standards, engineering best practices, and governance frameworks Drive performance optimization, scalability, and cloud cost optimization initiatives Collaborate with Architects, Product Owners, Analysts, and business stakeholders to translate business requirements into technical solutions Lead technical design discussions, code reviews, and architecture reviews Implement observability, monitoring, lineage, and data quality frameworks Mentor junior engineers and provide technical leadership to the team Ensure compliance with security, governance, and regulatory standards Support CI/CD and Infrastructure as Code implementations
Required Skills: 6–9 years of experience in Data Engineering Strong expertise in GCP data ecosystem and cloud-native architectures Deep hands-on experience with: Python PySpark SQL Apache Airflow / Cloud Composer dbt BigQuery
Strong understanding of data architecture patterns including: Lakehouse Architecture Medallion Architecture Data Mesh concepts Batch and Streaming architectures Experience with GCP services such as: BigQuery Dataproc Cloud Storage Pub/Sub Dataflow Composer IAM
Expertise in data modeling, partitioning, clustering, and query optimization Strong understanding of distributed systems and scalable data processing Experience with CI/CD, GitOps, Terraform, and containerization technologies Excellent stakeholder management and technical leadership skills
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