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Polars Data Jobs in Oregon (NOW HIRING)

Endava

... data engineering, or technical consulting - with at least 2+ years on Palantir Foundry * Strong proficiency in Python (PySpark, Pandas, Polars) and SQL; experience with TypeScript is highly valued

LMI

$70K - $104K/yr

Write queries and ensure proper data manipulation, retrieval, and storage for healthcare ... Proficiency with Python, including experience with libraries such as PySpark, Pandas, NumPy, Polars ...

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Polars Data information

What is the difference between Polars Data vs Data Analyst?

AspectPolars DataData Analyst
Required SkillsData manipulation, programming in Python/R, familiarity with data processing librariesData interpretation, reporting, visualization skills, basic programming
Work EnvironmentData processing, scripting, working with large datasetsBusiness analysis, presenting insights, collaborating with teams
Industry UsageData engineering, data science, analytics projectsBusiness intelligence, reporting, decision support

Polars Data focuses on efficient data processing and manipulation using programming tools, often in data engineering or data science contexts. Data Analysts primarily interpret data, create reports, and support business decisions. While both roles work with data, Polars Data is more technical and programming-oriented, whereas Data Analysts focus on analysis and communication of insights.

What are common challenges faced by professionals working with Polars Data, and how can they be addressed?

Professionals working with Polars Data often encounter challenges such as adapting to its unique API, optimizing data processing workflows for performance, and integrating Polars with other data tools. Since Polars is relatively new compared to libraries like pandas, there may be limited community support or documentation for complex use cases. To overcome these challenges, it's helpful to actively engage with the Polars community, regularly review official documentation, and experiment with different optimization strategies. Collaborating with team members familiar with similar data processing frameworks can also accelerate the learning curve.

What are the key skills and qualifications needed to thrive as a Polars Data engineer, and why are they important?

To thrive as a Polars Data Engineer, you need strong skills in data engineering, Python programming, and a solid understanding of the Polars library for efficient data processing. Familiarity with data pipeline tools, cloud platforms, and proficiency in using Polars for large-scale, high-performance data manipulation is typical, alongside knowledge of version control systems like Git. Analytical thinking, problem-solving, and effective communication are crucial soft skills for collaborating with teams and translating data needs into actionable solutions. These skills ensure you can design robust, scalable data workflows and deliver timely insights for data-driven decision-making.

What is a Polars Data professional?

Polars Data professionals are specialists who work with Polars, a fast DataFrame library designed for data manipulation and analysis, particularly in Python and Rust. They use Polars to efficiently process large datasets, perform data cleaning, transformation, and analysis tasks. These professionals often have backgrounds in data science, analytics, or software engineering, and choose Polars for its speed and scalability compared to traditional libraries like pandas. Their work is valuable in fields that require rapid data processing, such as finance, research, and technology.
What are popular job titles related to Polars Data jobs in Oregon? For Polars Data jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Polars Data jobs in Oregon look for? The top searched job categories for Polars Data jobs in Oregon are:
Polars Data jobs near you

Software Engineer - ML Platform (Staff / Sr Staff)

Equilibrium Energy

OR • On-site, Remote

Other

Re-posted 2 days ago

Job description

What we are looking for

Our power sector is in the middle of a major transformation. Its increasingly renewable resource mix and demand-side changes require algorithmic management far beyond what was historically required. Because of this, scalable model development and deployment is at the heart of what EQ does. We are looking for Staff / Sr Staff Software Engineers who are passionate about helping to deliver this scientific platform - to stay at the forefront of AI/ML technology and operationalize those solutions at enterprise scale.

What you will do

You will be a member of EQ's Science Platform team. Our Science Platform enables our internal data scientists, as well as external customers, to develop, experiment with, deploy, and monitor forecasting and optimization models at scale. We sit between our data and infra engineers and our scientists - developing frameworks for model development that are both robust and efficient to iterate within. We help bring the algorithmic capabilities of our scientists to a broad range of customer energy applications.

Key Responsibilities:

  • Abstract away the complexities behind the deployment and orchestration of a large number of forecasting workflows, enabling a fast model development lifecycle for our Science team
  • Integrate with data and compute infrastructure to optimize resource utilization and performance
  • Implement automated testing and monitoring for ML models in production
  • Maintain and iterate on our model registry and experiment tracking
  • Co-design frameworks that support model experimentation, hyperparameter tuning, training, and deployment
  • Partner with our Data Services team to incrementally improve our feature store and tie it to the EQ ontology
  • Collaborate closely with data scientists to understand new model requirements and together implement solutions that are robust, validated, and scalable
  • Collaborate with the Science Platform Simulation team to incorporate forecasting into physical and portfolio asset optimizations
  • Partner with our Product and Customer Delivery teams to enable external customers to perform similar tasks to our internal scientists, with minimal code divergence and following security best practices
  • Stay up-to-date with the latest advancements in ML engineering and integrate best practices into the platform
The minimum qualifications you'll need
  • A commitment to clean energy and combating climate change
  • Proficiency and 5+ years experience in Python software development
  • Familiarity with automated build, deployment, and orchestration tools such as CI/CD, Pants, Docker, Metaflow, Argo, and Kubernetes
  • Strong understanding of data pipelines, ETL, and data infrastructure
  • Experience with observability tooling like Grafana, Honeycomb, and Prometheus
  • Experience with common machine learning algorithms and libraries (xgboost, sklearn, pytorch, pandas, polars, pandera)
  • Prior experience in operationalizing machine learning workflows
  • Agility in working with cross-functional teams and adapting to new work methodologies
  • Familiarity with agile practices, or a willingness to learn
  • Strong communication skills for collaborating within a remote-first team that works internationally across timezones
Nice-to-have additional skills
  • An advanced degree in computer science or machine learning
  • Experience in time series forecasting
  • Experience building tools that support data scientists
  • Experience with Databricks and Spark or Dagster
  • Background in the energy and power systems sector

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