Google Cloud Platform Data Engineer with MLOPS - Philadelphia, PA (Hybrid)

Overview

Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - 6 Month(s)

Skills

GCP
Data Engineer
MLOPS
Terraform
Jenkins
CI/CD
kafka
docker
kubernetes

Job Details

Role: Google Cloud Platform Data Engineer

Duration: 6+ months
Location: Philadelphia, PA (Hybrid)

Experience: 5+ years

Job description:

We are seeking a highly skilled Data Engineer to design, build, and maintain scalable data platforms that enable large-scale ingestion, storage, processing, and analysis of structured and unstructured data. This role will focus on constructing data products (data lake / data warehouse), optimizing data pipelines, and implementing robust ETL workflows to support analytics, machine learning, and operational reporting.

The ideal candidate will be proficient in distributed computing, cloud-based data architectures (Google Cloud Platform), and modern data processing frameworks. Experience with real-time data streaming (Kafka, Apache Beam), MLOps, and infrastructure automation (Terraform, Jenkins) is highly preferred. Google Cloud including GitHub, Docker, and Kubernetes experience is a must.

Key Responsibilities:

Data Platform & Architecture Development

  • Design, implement, and maintain scalable data platforms for efficient data storage, processing, and retrieval.
  • Build cloud-native and distributed data systems that enable self-service analytics, real-time data processing, and AI-driven decision-making.
  • Develop data models, schemas, and transformation pipelines that support evolving business needs while ensuring operational stability.
  • Apply best practices in data modeling, indexing, and partitioning to optimize query performance, cost efficiency, considering best practices for Sustainability.

ETL, Data Pipelines & Streaming Processing

  • Build and maintain highly efficient ETL pipelines using SQL and Python, to process large-scale datasets.
  • Implement real-time data streaming pipelines using Kafka, Apache Beam, or equivalent technologies.
  • Develop reusable internal data processing tools to streamline operations and empower teams across the organization.
  • Write advanced SQL queries for extracting, transforming, and loading (ETL) data with a focus on execution efficiency.
  • Ensure data validation, quality monitoring, and governance using automated processes and dashboards.

MLOps & Cloud-Based Data Infrastructure

  • Deploy machine learning pipelines with MLOps best practices to support AI and predictive analytics applications.
  • Optimize data pipelines for ML models, ensuring seamless integration between data engineering and machine learning workflows.
  • Work with cloud platforms (Google Cloud Platform) to manage data storage, processing, and security.
  • Utilize Terraform, Jenkins, CI/CD tools to automate data pipeline deployments and infrastructure management.

Collaboration & Agile Development

  • Work in Agile/DevOps teams, collaborating closely with data scientists, software engineers, and business stakeholders.
  • Advocate for data-driven decision-making, educating teams on best practices in data architecture and engineering.

Required Skills & Qualifications

  • 5+ years of experience as a Data Engineer working with large-scale data processing.
  • Strong proficiency in SQL for data transformation, optimization, and analytics.
  • Expertise in programming languages (Python, Java, Scala, or Go) with an understanding of functional and object-oriented programming paradigms.
  • Experience with distributed computing frameworks.
  • Proficiency in cloud-based data engineering on AWS, Google Cloud Platform, or Azure.
  • Strong knowledge of data modeling, data governance, and schema design.
  • Experience with CI/CD tools (Jenkins, Terraform) for infrastructure automation.

Preferred Qualifications

  • Experience with real-time data streaming (Kafka, or equivalent).
  • Strong understanding of MLOps and integrating data engineering with ML pipelines.
  • Familiarity with knowledge graphs and GraphQL APIs for data relationships.
  • Background in retail, customer classification, and personalization systems.
  • Knowledge of business intelligence tools and visualization platforms.
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