What are the responsibilities and job description for the Google Cloud Platform Data Engineer with Dataplex and AI (Last round In - person) position at Jobs via Dice?
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Rivago infotech inc, is seeking the following. Apply via Dice today!
Fine With Relocation
Interview Mode: One video interview and the client interview will be in person at any Client office
Role : Google Cloud Platform Data Engineer with Dataplex and AI (Last Round In-person)
Location : Irving, Texas 75039 (100% onsite)
Hire type : Contract
Implementation partner -**********
End Client - Banking / Finance
Experience: 9 years
Must have : Dataplex exp
Overview
We are seeking a Google Cloud Platform Data Engineer with deep, hands-on architectural and development
experience in Google Cloud Platform’s big data ecosystem. You will be responsible for
designing, building, and optimizing a modern data lakehouse architecture. Your primary focus
will be leveraging BigLake, BigQuery, Google Cloud Storage (GCS), and Vertex AI to create
seamless, scalable data pipelines and machine learning integrations that drive business
intelligence and predictive analytics.
Key Responsibilities
Lakehouse Architecture & Development:
Architect and maintain a scalable data lakehouse using Google Cloud Storage
(GCS) as the foundational data lake and BigLake to unify data warehouses and data lakes.
Implement fine-grained security (row-level and column-level access controls) and
data governance across open file formats (Parquet, Iceberg, ORC) using BigLake.
Data Warehousing & Optimization:
Design and manage complex, highly scalable data models within Big Query.
Perform deep performance tuning and cost optimization of Big Query jobs utilizing
clustering, partitioning, materialized views, and slot capacity management.
AI/ML Integration & MLOps:
Collaborate with Data Scientists to operationalize machine learning models using
Vertex AI.
Build robust data pipelines to feed Vertex AI Feature Store, manage model
training workflows and deploy ML models into production.
Utilize Big Query ML (BQML) for in-database predictive modeling and analytics
where appropriate.
Data Pipeline Engineering:
Design, develop, and orchestrate batch and streaming data pipelines (using tools
like Dataflow, Dataproc, or Cloud Composer/Airflow) to ingest data from diverse
sources into GCS and BigQuery.
Data Governance & Best Practices:
Establish data lifecycle management policies in GCS.
Ensure data quality, reliability, and security compliance across the entire Google Cloud Platform big
data stack.
Mentor junior engineers and lead code/architecture reviews.
Required Qualifications
Experience: 5 years of dedicated Data Engineering experience, with at least 3 years
focused exclusively on the Google Cloud Platform (Google Cloud Platform).
Deep Google Cloud Platform Big Data Expertise:
BigQuery: Expert-level knowledge of BigQuery architecture, advanced SQL,
analytical functions, query profiling, and optimization techniques.
BigLake: Proven experience utilizing BigLake for multi-cloud or lakehouse
architectures, managing open-source formats (e.g., Apache Iceberg/Parquet),
and enforcing unified security policies.
GCS: Deep understanding of GCS storage classes, object lifecycle management,
and optimizing GCS for big data workloads.
Vertex AI: Hands-on experience with Vertex AI pipelines, endpoints, feature
stores, or deploying ML models into scalable data environments.
Programming Skills: Advanced proficiency in Python and SQL. Familiarity with Java,
Scala, or Go is a plus.
Data Orchestration & CI/CD: Experience with orchestration tools (e.g., Apache Airflow,
Cloud Composer) and modern CI/CD pipelines (e.g., GitHub Actions, Terraform, Cloud
Build).
Preferred/Bonus Qualifications
Google Cloud Platform Certifications: Google Cloud Certified - Professional Data Engineer or
Professional Machine Learning Engineer.
Fine With Relocation
Interview Mode: One video interview and the client interview will be in person at any Client office
Role : Google Cloud Platform Data Engineer with Dataplex and AI (Last Round In-person)
Location : Irving, Texas 75039 (100% onsite)
Hire type : Contract
Implementation partner -**********
End Client - Banking / Finance
Experience: 9 years
Must have : Dataplex exp
Overview
We are seeking a Google Cloud Platform Data Engineer with deep, hands-on architectural and development
experience in Google Cloud Platform’s big data ecosystem. You will be responsible for
designing, building, and optimizing a modern data lakehouse architecture. Your primary focus
will be leveraging BigLake, BigQuery, Google Cloud Storage (GCS), and Vertex AI to create
seamless, scalable data pipelines and machine learning integrations that drive business
intelligence and predictive analytics.
Key Responsibilities
Lakehouse Architecture & Development:
Architect and maintain a scalable data lakehouse using Google Cloud Storage
(GCS) as the foundational data lake and BigLake to unify data warehouses and data lakes.
Implement fine-grained security (row-level and column-level access controls) and
data governance across open file formats (Parquet, Iceberg, ORC) using BigLake.
Data Warehousing & Optimization:
Design and manage complex, highly scalable data models within Big Query.
Perform deep performance tuning and cost optimization of Big Query jobs utilizing
clustering, partitioning, materialized views, and slot capacity management.
AI/ML Integration & MLOps:
Collaborate with Data Scientists to operationalize machine learning models using
Vertex AI.
Build robust data pipelines to feed Vertex AI Feature Store, manage model
training workflows and deploy ML models into production.
Utilize Big Query ML (BQML) for in-database predictive modeling and analytics
where appropriate.
Data Pipeline Engineering:
Design, develop, and orchestrate batch and streaming data pipelines (using tools
like Dataflow, Dataproc, or Cloud Composer/Airflow) to ingest data from diverse
sources into GCS and BigQuery.
Data Governance & Best Practices:
Establish data lifecycle management policies in GCS.
Ensure data quality, reliability, and security compliance across the entire Google Cloud Platform big
data stack.
Mentor junior engineers and lead code/architecture reviews.
Required Qualifications
Experience: 5 years of dedicated Data Engineering experience, with at least 3 years
focused exclusively on the Google Cloud Platform (Google Cloud Platform).
Deep Google Cloud Platform Big Data Expertise:
BigQuery: Expert-level knowledge of BigQuery architecture, advanced SQL,
analytical functions, query profiling, and optimization techniques.
BigLake: Proven experience utilizing BigLake for multi-cloud or lakehouse
architectures, managing open-source formats (e.g., Apache Iceberg/Parquet),
and enforcing unified security policies.
GCS: Deep understanding of GCS storage classes, object lifecycle management,
and optimizing GCS for big data workloads.
Vertex AI: Hands-on experience with Vertex AI pipelines, endpoints, feature
stores, or deploying ML models into scalable data environments.
Programming Skills: Advanced proficiency in Python and SQL. Familiarity with Java,
Scala, or Go is a plus.
Data Orchestration & CI/CD: Experience with orchestration tools (e.g., Apache Airflow,
Cloud Composer) and modern CI/CD pipelines (e.g., GitHub Actions, Terraform, Cloud
Build).
Preferred/Bonus Qualifications
Google Cloud Platform Certifications: Google Cloud Certified - Professional Data Engineer or
Professional Machine Learning Engineer.