Data Engineer

2 weeks ago

Irving, United States Resource Informatics Group Full time
Position: Data Engineer
Location: 100% Remote
Duration: 6+ Months
Experience: - 8+ Years

Technologies We Use:
AWS Serverless Services Stack (S3, Athena, Lambda, SQS, SNS, Step Functions, Redshift, Glue, etc.),
Python
SQL
Bitbucket/Github
MSSQL Server
Salesforce.

Your Role:
  • Execute on a shared technical architectural direction, long-term vision, and guiding principles for data and platforms and ensure alignment with the enterprise technology strategy.
  • Design, build out and modify AWS data integration pipelines to modernize and harden process models for extracting data from operational platforms into data lake.
  • Build and manage data models (relational, dimensional & NoSQL) to support standardized processing environment. Build deep understanding of how dozens of data sources can be combined for 360-view.
  • Provide support for and own enterprise integrations (i.e. Salesforce, AWS Cognito, Enterprise APIs, Google Analytics, Ecommerce, etc.)
  • Develop integration solutions in the incubation phase and build prototypes as necessary to validate architecture approaches.
  • Develop and maintain SQL queries to support aggregations used for reporting and marketing automation.
  • Maintain high standards of process documentation. Provide non-functional requirements according to which the solution is defined, managed, and delivered.
  • Partner with architecture and engineering leaders to drive alignment on the right balance between consistency and flexibility in our cloud adoption approach.
  • Mentor, educate and train colleagues as requested.

Qualifications
Required Experience:
  • 3+ years of experience with architecting and engineering AWS cloud-based Data solutions.
  • 7+ years of experience in Data Engineering for mid-size to large corporations.
  • Experience supporting and working with data platforms.
  • Extensive Python/C# coding experience in application to data processing.
  • Excellent SQL skills.
  • Experience in data modeling (relational, dimensional & NoSQL)
  • Experience in building data integration pipelines for transitional and analytical applications
  • Experience with cloud-native data and parallel data processing architectures (Data Lake architectures, MPP database engines, serverless data processing pipelines, Spark, etc.)
  • Experience consuming data from various sources (databases, file sources, REST services, SOAP services, web-scraping, etc.) and various formats (csv, tsv, json, xml, html, unstructured text, etc.) in Python or C#
  • Execution of DevOps methodologies and Continuous Integration/Continuous Delivery (CI/CD)
  • Experience with Agile (SCRUM, Kanban, etc.) or lean development and able to discuss workflows for different software development processes.
  • Attention to detail and results oriented, with a strong product and customer focus.
  • Strong analytical and problem-solving skills.
  • The ability to work in a team environment.
  • Excellent verbal and written communication skills with the ability to talk to technical and non-technical people, including executive management.
  • Drive to learn and keep pace with the latest advances in the field; rapidly grasp new technologies to support the environment and contribute to project deliverables.

    What We Look For:
  • Experience with products in cloud deployment.
  • Experience building and implementing scalable applications that use modern design patterns that leverage Cloud platforms.
  • Leveraging domain driven design principles to support legacy re-platforming efforts.
  • Designing message-based interaction models to support distributed data across the enterprise.
  • Excellent problem-solving skills.
  • Strong development skills - must be able to work "hands-on."
  • Strong team player who enjoys working in a fast-paced atmosphere
  • Ability to manage multiple priorities, commitments and projects and organize effectively.
  • Self-motivated and passionate about what you do.
  • Strong written and verbal communication skills.

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