Google Cloud Platform Data Engineer with Dataplex and AI (Last round In - person)

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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.