We’re seeking an experienced GCP Data Engineer who can build cloud analytics platform to meet ever expanding business requirements with speed and quality using lean Agile practices. You will work on analyzing and manipulating large datasets supporting the enterprise by activating data assets to support Enabling Platforms and Analytics in the Google Cloud Platform (GCP). You will be responsible for designing the transformation and modernization on GCP, as well as landing data from source applications to GCP. Experience with large scale solution and operationalization of data warehouses, data lakes and analytics platforms on Google Cloud Platform or other cloud environment is a must. We are looking for candidates who have a broad set of technology skills across these areas and who can demonstrate an ability to design right solutions with appropriate combination of GCP and 3rd party technologies for deploying on Google Cloud Platform.
Required Skills and Selection Criteria:
In-depth understanding of Google’s product technology (or other cloud platform) and underlying architectures 5+ years of analytics application development experience required 5+ years of SQL development experience 3+ years of Cloud experience (GCP preferred) with solution designed and implemented at production scale Experience working in GCP based Big Data deployments (Batch/Real-Time) leveraging Terraform, Big Query, Big Table, Google Cloud Storage, PubSub, Data Fusion, Dataflow, Dataproc, Cloud Build, Airflow, Cloud Composer etc. or equivalent technology Good understanding of domain driven design and data mesh principles. Strong understanding on DevOps principles and practices, including continuous integration and deployment (CI/CD), automated testing & deployment pipelines. Good understanding of cloud security best practices and be familiar with different security tools and techniques like Identity and Access Management (IAM), Encryption, Network Security, etc. Strong understanding of microservices architecture.
Nice to Have
Bachelor’s degree in Computer science/engineering, Data science or related field. Strong leadership, communication, interpersonal, organizing, and problem-solving skills Good presentation skills with ability to communicate architectural proposals to diverse audiences (user groups, stakeholders, and senior management). Experience in Banking and Financial Regulatory Reporting space. Ability to work on multiple projects in a fast paced & dynamic environment. Exposure to multiple, diverse technologies, platforms, and processing environments. Google Professional Cloud Data Engineering certification. Experience in migrating legacy analytics applications to Cloud platform and business adoption of these platforms to build insights and dashboards through deep knowledge of traditional and cloud Data Lake, Warehouse and Mart concepts. Utilize Google Cloud Platform & Data Services to modernize legacy applications. Understand technical business requirements and define engineering solutions that align to Ford Motor & Credit Companies Patterns and Standards. Collaborate and work with global engineering teams to define analytics cloud platform strategy and build Cloud analytics solutions within enterprise data factory. Provide Engineering leadership in design & delivery of new Unified data platform on GCP. Understand complex data structures in analytics space as well as interfacing application systems. Develop and maintain conceptual, logical & physical data models. Design and guide Product teams on Subject Areas and Data Marts to deliver integrated data solutions. Provide technical guidance for optimal solutions considering regional Regulatory needs. Provide technical assessments on solutions and make recommendations that meet business needs and align with architectural governance and standard. Guide teams through the enterprise processes and advise teams on cloud-based design, development, and data mesh architecture. Provide advisory and technical consulting across all initiatives including PoCs, product evaluations and recommendations, security, architecture assessments, integration considerations, etc. Leverage cloud AI/ML Platforms to deliver business and technical requirements.