Description

You will design and maintain a scalable data lakehouse on Google Cloud Platform, leveraging BigLake, BigQuery, and Vertex AI to drive business intelligence and predictive analytics.

Responsibilities

• Architect the data lakehouse using Google Cloud Storage and BigLake, enforcing fine-grained security and governance on open formats like Parquet and Iceberg.
• Design complex data models in BigQuery, performing deep performance tuning and cost optimization through clustering, partitioning, and materialized views.
• Operationalize machine learning models using Vertex AI, building pipelines for the Feature Store and deploying models into production.
• Develop and orchestrate batch and streaming data pipelines using Dataflow, Dataproc, or Cloud Composer to ingest data into GCS and BigQuery.
• Establish data lifecycle policies, ensure data quality compliance, and mentor junior engineers through code and architecture reviews.

Required Skills

• 5+ years of Data Engineering experience, with at least 3 years focused exclusively on Google Cloud Platform.
• Expert-level BigQuery knowledge, including advanced SQL, query profiling, and optimization techniques.
• Proven experience with BigLake for lakehouse architectures, managing open-source formats, and unified security policies.
• Hands-on experience with Vertex AI pipelines, endpoints, feature stores, or ML model deployment.
• Advanced proficiency in Python and SQL.
• Experience with orchestration tools like Apache Airflow or Cloud Composer.
• Familiarity with CI/CD pipelines using GitHub Actions, Terraform, or Cloud Build.
• Deep understanding of GCS storage classes, object lifecycle management, and optimization for big data workloads.

Preferred Skills

• Google Cloud Certified - Professional Data Engineer or Professional Machine Learning Engineer.
• Familiarity with Java, Scala, or Go.

Key Skills

Education

Any Graduate