Hiring: Senior Data Engineer (GCP + Databricks) | 68 Years | High-Impact Role
Are you passionate about building scalable data platforms and working with cutting-edge cloud technologies
We're looking for a Senior Data Engineer who can turn complex data into powerful business insights.
About the Role
As a Senior Data Engineer, you will design, build, and optimize modern data platforms that power analytics, AI, and business decision-making. You'll work closely with data scientists, analysts, and product teams to deliver reliable and scalable data solutions.
Key Responsibilities
- Design and build scalable ETL/ELT pipelines using cloud-native technologies
- Develop and optimize data workflows in GCP (BigQuery, Dataflow, Pub/Sub, Dataplex)
- Work on Databricks ecosystem including Delta Lake, DLT, and Unity Catalog
- Implement data modeling techniques (Star Schema, Snowflake, 3NF, denormalized models)
- Build and maintain high-performance data pipelines for large-scale processing
- Ensure data quality, governance, and metadata management
- Collaborate with cross-functional teams to deliver data-driven solutions
- Support AI/ML pipelines and enable data science use cases
- Ensure compliance with data security and governance standards (SOX/PCI)
Must-Have Skills
- 68 years of experience in Data Engineering / Data Platform Development
- Strong hands-on experience in GCP ecosystem
- Experience with Databricks (Delta Lake, DLT, Unity Catalog)
- Expertise in data architectures: Data Lake, Data Warehouse, Lakehouse
- Strong knowledge of data modeling & pipeline optimization
- Experience with ETL/ELT frameworks and big data processing
- Solid understanding of data governance & quality frameworks
- Good communication skills and ability to translate business requirements into technical solutions
Good to Have
- Exposure to Data Mesh / Domain-driven architecture
- Experience with modern data stack (dbt, Terraform, Cloud Composer, Dataplex, Atlan)
- Knowledge of data cataloging & lineage tools
- Understanding of FinOps (cloud cost optimization)
- Exposure to AI/ML pipelines, LLMs, or AI-powered analytics
- Experience in product-based or large enterprise environments
