Senior Google Cloud Platform Data Engineer/ Architect

Role: Senior Google Cloud Platform Data Engineer/ Architect Locations: Philadelphia, PA (Fully Onsite) Duration: 12 Months Contract

Note: Candidate needs to be in the office 5 Days every week. Local or candidates from Nearby states only.

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.

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, 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.