Senior Data Engineer II
About The Position
Perform daily data loads, ensuring recurring updates are logged and tracked. Interface with other technical personnel or team members to document, interpret and finalize requirements. Produce code that is efficient, repeatable, without defects, and adherent to best practices such as naming conventions, and encapsulation. Write and review portions of detailed specifications for the development of system components of moderate complexity. Complete complex data engineering bug fixes and resolve related issues, researching and identifying root causes as appropriate. Work closely with other development team members to understand complex product requirements and translate them into data engineering and/or data management designs. Innovate process improvements that enable efficient delivery and maintenance. Successfully implement development processes, coding best practices, and code reviews. Operate in various development environments (including Agile and Waterfall) while collaborating with key stakeholders. Identify areas where it is an advantage to work with other teams to improve overall quality, and with peers or others, implement initiatives improving capabilities and efficiency. Train entry-level data engineers as directed by department management, ensuring they are knowledgeable in critical aspects of their roles, and mentor junior data engineers on methodologies and optimization techniques. Keep abreast of new technology developments. Design and work with complex data models. Perform other duties as needed.
Requirements
- Bachelor’s degree (or foreign equivalent) in Computer Science, Computer Engineering, Information Systems, or a related field required.
- 5 years of experience in job offered or related occupations required.
- 5 years of experience with ML-Based Application Development & API Integration to build software features that rely on machine-learning models and to integrate those models into applications through APIs so the system can send data and receive predictions automatically.
- 5 years of experience to convert research-grade ML models into stable, scalable services that other applications and users can access reliably.
- 5 years of experience building and deploying machine learning-based applications, including the design and integration of ML APIs and RESTful for scalable, production-grade systems.
- 5 years of experience with data engineering & ETL Pipelines to design automated workflows that extract data from multiple sources, transform it into usable formats, and load it into databases or ML systems, ensuring that large volumes of structured and unstructured data are cleaned, validated, and delivered reliably for analytical and machine-learning purposes.
- 5 years of experience designing and maintaining ETL workflows for large-scale data processing, including structured and unstructured data.
- 5 years of experience utilizing database & Vector Store Technologies.
- 5 years of experience using SQL/NoSQL databases and/or vector databases such as Solr, Elasticsearch, or Pinecone.
- 5 years of experience with Cloud Infrastructure, Deployment Automation & Containerization to deploy and operate applications on cloud platforms using automated tools to ensure systems are scalable, consistent, and easy to maintain.
- 5 years of experience deploying applications on cloud platforms including AWS, Azure, and GCP, using infrastructure-as-code tools such as Terraform, and containerization technologies including Docker.
- 5 years of experience utilizing orchestration tools such as Kubernetes to automatically manage, deploy, and scale containerized applications across multiple servers to ensure applications remain available, recover quickly from failures, and can be updated or rolled back safely without interrupting service.
- 5 years of experience with MLOps & Model Deployment.
- 5 years of experience with MLOps practices and deploying ML models using frameworks such as KubeRay, Triton Inference Server, or similar orchestration tools.
Responsibilities
- Perform daily data loads, ensuring recurring updates are logged and tracked.
- Interface with other technical personnel or team members to document, interpret and finalize requirements.
- Produce code that is efficient, repeatable, without defects, and adherent to best practices such as naming conventions, and encapsulation.
- Write and review portions of detailed specifications for the development of system components of moderate complexity.
- Complete complex data engineering bug fixes and resolve related issues, researching and identifying root causes as appropriate.
- Work closely with other development team members to understand complex product requirements and translate them into data engineering and/or data management designs.
- Innovate process improvements that enable efficient delivery and maintenance.
- Successfully implement development processes, coding best practices, and code reviews.
- Operate in various development environments (including Agile and Waterfall) while collaborating with key stakeholders.
- Identify areas where it is an advantage to work with other teams to improve overall quality, and with peers or others, implement initiatives improving capabilities and efficiency.
- Train entry-level data engineers as directed by department management, ensuring they are knowledgeable in critical aspects of their roles, and mentor junior data engineers on methodologies and optimization techniques.
- Keep abreast of new technology developments.
- Design and work with complex data models.
- Perform other duties as needed.
Benefits
- standard company benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
