Senior Data Engineer Consultant

RGP•Mountain View, CA

5d•$90 - $110•Onsite

About The Position

RGP is seeking a highly experienced Senior Data Engineer Consultant to design, build, and optimize scalable data platforms and pipelines supporting analytics, business intelligence, and AI initiatives. This role will work across data engineering, cloud platforms, data architecture, and AI-enabled workflows, partnering closely with technical and business stakeholders to deliver reliable, high-performing data solutions. The ideal candidate brings 10+ years of data engineering experience, strong hands-on expertise with Python, SQL, GCP, BigQuery, PySpark, Airflow, and streaming technologies, along with experience leading data engineering teams and complex projects from requirements through implementation and open to onsite project work.

Requirements

10+ years of experience in Data Engineering, Data Warehousing, or related disciplines.
Strong hands-on experience with Python and SQL.
Strong experience with GCP and BigQuery.
Experience with PySpark, Apache Airflow/GCP Composer, Dataflow, Pub/Sub, or Kafka.
Strong understanding of dimensional modeling, data warehousing, ETL/ELT, and data architecture.
Experience working with high-volume data environments and performance optimization.
Experience gathering requirements and delivering data solutions end-to-end.
Experience leading technical projects or small-to-medium-sized engineering teams.
Strong communication skills with the ability to work with both technical and business stakeholders.

Nice To Haves

Google Cloud Professional Data Engineer certification.
Experience supporting AI/ML data products or AI-enabled workflows.
Experience with Looker, QlikView, SAP BusinessObjects, or other BI platforms.
Experience with Oracle/Exadata and legacy data warehouse modernization.
Experience with CI/CD tools such as Git, TeamCity, or similar platforms.
Experience working in Agile/Scrum environments.
Experience developing automated data quality, observability, or pipeline governance frameworks.
Experience with tools such as OpenAI Codex, Claude Code, GitHub Copilot, or similar AI development technologies is highly desirable.

Responsibilities

Design and develop scalable, enterprise-grade data pipelines and data platforms.
Build batch and real-time data processing solutions using PySpark, Dataflow, Kafka, Pub/Sub, and Apache Airflow/GCP Composer.
Develop data models, dimensional models, and analytical data layers to support reporting, BI, and AI use cases.
Design solutions across structured and unstructured data environments, including BigQuery, GCS, Hive, HDFS, and Parquet.
Gather technical requirements and translate business needs into scalable data solutions.
Develop and optimize cloud-based data solutions within Google Cloud Platform (GCP).
Build and maintain BigQuery data environments and migrate legacy data platforms into cloud architectures.
Optimize data processing, storage, and query performance for high-volume datasets.
Develop solutions capable of processing millions or billions of records while maintaining reliability and scalability.
Integrate modern AI tooling into data engineering workflows to improve development, automation, data quality, and pipeline management.
Support data products and analytical solutions that enable AI/ML model measurement and business insights.
Identify opportunities to use AI to improve engineering productivity, pipeline monitoring, data quality, and remediation processes.
Identify and resolve pipeline performance, scalability, and data quality issues.
Implement best practices around data validation, observability, resiliency, and monitoring.
Optimize ETL/ELT processes and analytical queries to improve processing time and system performance.
Establish standards for data architecture, modeling, performance, and pipeline development.
Lead small to medium-sized data engineering projects and technical initiatives.
Provide technical leadership and mentorship to other data engineers.
Partner with business stakeholders, architects, analysts, data scientists, and engineering teams.
Participate in requirements gathering, solution design, technical documentation, development, testing, and deployment.
Develop project plans and provide technical guidance to ensure successful delivery.