Data Engineer (I)

Work Location: Portland, OR - Remote

Work Schedule: Monday - Friday, Normal Business Hours

Top Day to Day Responsibilities:

- Build an agentic pipeline for data processing, analysis, visualization, as well as tools for model analysis and debugging.

- Clean, structure, and catalog raw spatial/research datasets to ensure high data quality.

- Coordinate with MAXO researchers to provide processed data outputs.

- Coordinate with a team in India (potentially work odd hours) to communicate task expectation, enqueue and dequeue annotation jobs, analyse annotation quality and provide feedback to the rating team.

Skills Needed:

- Solid programming skills (with python experience), especially on building tools, web UIs, scripts etc for data collection, annotation, and processing.

- Data science and engineering skills, including data quality verification, building agentic data processing pipelines, data visualization, and data analysis.

- Experienced in conducting human studies or surveys.

- Solid communication skills with the ability to discuss technical designs with researchers/SWEs, as well as discuss data annotation with non-technical raters.

- Knowledge of VLM evaluation, multi-modal GenAI model (e.g., image generation, video generation) evaluation is a plus.

Description

As a Data Engineer, you execute complete, tactical tasks within the scope of a larger project with guidance from manager, team drive, or senior colleagues, within bounded time. You apply standard tools, resources, and processes to solve defined problems, and work on developing working relationships outside the team to contribute to cross-project collaborations. You possess a foundational understanding of core data and role-related knowledge, relevant our technologies, and processes and translate business requirements/needs into technical data solutions. Responsibilities include:
- Create and/or consult in creating data visualizations, visualization features using internal BI tools like PLX, Datastudio, and external tools like Tableau and Looker, with some guidance.
- Provide ongoing support for data users through maintenance of reports, queries, and dashboards with some guidance.
- Perform exploratory data analysis and profiling utilizing relevant tools, leveraging custom data infrastructure, or existing data models with some guidance. Work with clients to understand their needs and clarify details of requirements. Enable data-driven decision-making by collecting, transforming, and publishing data.
- Leverage, deploy, and continuously train pre-existing machine learning models. Learn and implement new storage/MPP systems/ML serving systems, with some guidance.
- Implement business solutions and infrastructure to build and scale common frameworks for use with some guidance. Seek and follow local technical best practices, including making data discoverable, thinking about the lifecycle of data, and managing master data well. Design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance, scalability and efficiency, reliability and fidelity, and flexibility and portability.
- Develop and maintain data models, pipelines, and exchange formats to assist in the visualization, analysis, interpretation of data and for use of data in ML training/models, with direct guidance.
- Consult with users, partners, or decisions makers to identify data sources, required data elements, or data validation standards with some guidance. Consult with application engineers to understand and influence logging/transactional storage. Consult with Data scientists on ML training, feature engineering for ML models.Minimum role qualification requires proficiency in:
- Code comprehension and programming skills
- Information gathering skills
- Project management
- Data exploration
- Big data infrastructure
- Machine Learning Knowledge
- Stakeholder management
- Data pipeline(ETL) design and Data Modeling
- Statistics & BI tools

EEO:

Mindlance is an Equal Opportunity Employer and does not discriminate in

employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.