Location: Wilmington, DE
Charlotte, NC
Chandler, AZ
Irving, TX
Philadelphia, PA
Duration: 24 months
Work Engagement: W-2
Benefits on offer for this contract position: Health Insurance, Life insurance, 401K and Voluntary Benefits

SUMMARY:

In this contingent resource assignment, you may: Consult on complex initiatives with broad impact and large-scale planning for Specialty Software Engineering. Review and analyze complex multi-faceted, larger scale, or longer-term Specialty Software Engineering challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented factors. Contribute to the resolution of complex and multi-faceted situations requiring solid understanding of the function, policies, procedures, and compliance requirements that meet deliverables. Strategically collaborate and consult with client personnel.

QUALIFICATIONS:

• Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
• 5+ years of experience in data engineering including hands-on experience working with Hadoop and Google Cloud data solutions: creating/supporting Spark based processing, Kafka streaming, in a highly collaborative team
• 3+ years of experience with Data lakehouse architecture and design, including hands-on experience with Python, pySpark, Apache Kafka, Airflow, and SQL, GPC Cloud Storage, BigQuery, Data Proc, Cloud Composer
• 2+ years working with NoSQL databases such as columnar databases, graph databases, document databases, KV stores, and associated data formats
• Public cloud certifications such as GCP Professional Data Engineer, Azure Data Engineer, or AWS Specialty Data Analytics

DESIRED QUALIFICATIONS:

• Proven skills with data migration from on-prem to a cloud native environment
• Proven experience working with the Hadoop ecosystem capabilities such as Hive, HDFS, Parquet, Iceberg, and Delta Tables
• Deep understanding of data warehouse, data cloud architecture, building data pipelines, and orchestration
• Design and implementation of highly scalable and modular data pipelines with built-in data controls for automating data governance
• Familiarity of GenAI frameworks such as Langchain and Langraph to develop agent-based data capabilities
• Dev Ops and CI/CD deployments including Git, Jenkins, Docker, and Kubernetes
• Web based UI development using React and Node JS is a plus

Are you looking for a chance to be part of a high performing team that is passionate about data? Does the thought of building a modern hybrid data platform from the ground up excite you? We are looking for someone to join our team to help enable solutions for the next generation of data analysis while working in a fast-paced environment that fosters growth and development. If this sounds like you, we want to hear from you.
We are currently seeking a Lead Cloud Data Platform Engineer to apply deep technical skills to create data products, develop AI-based automation tools, and build a world-class cloud analytics capability for Wells Fargo’s Cyber Security Data Ecosystem on a Hybrid cloud. The successful candidate will continually innovate and pioneer the use of new technologies, and drive adoption of these amongst a team of talented data engineers. You will be an integral part of our migration from our on-premises systems to the Google Azure cloud. You will be a vital member of an agile team helping to lead the design and hands-on implementation of modern data processing capabilities. This is a visible role that allows you to share your knowledge and skills with other developers and product teams in a collaborative environment.

In this role you will

• Implement and operationalize modern self-serve data capabilities on Google Cloud to ingest, transform, and distribute data for a variety of big data apps
• Enable secure data pipelines to ensure data protection in transit and at rest
• Automate data governance capabilities to ensure proper data observability throughout the data flows
• Leverage AI/Agentic frameworks to automate data management, governance, and data consumption capabilities
• Create repeatable processes to instantiate data processes that fuel analytics products and business decisions
• Work with principal engineers, product managers, and data engineers to roadmap, plan, and deliver key data capabilities based on priority
• Create the Future of Data: design and implement processes using the entire toolset within GCP to shape the future of data