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Data Engineer
IPRally Technologies Oy. Learn what the data means, identify shortcomings, and model relationships among patents, research papers, and companies .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and running Python data pipelines, particularly using Dagster, while ensuring data quality and collaborating effectively with cross-functional teams. Possesses strong analytical skills to model relationships among diverse data sources and evaluate their quality.
Highest-signal resume keywords
Python Data Pipeline DevelopmentData Quality MonitoringStatistical Evaluation BuildingCollaboration With Product TeamsMachine Learning Processing
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Data Pipeline DevelopmentStatistical EvaluationData Processing SystemsMachine LearningData Quality ChecksData ModelingData AnalysisData EvaluationAgent Harness BuildingDistributed Processing
Soft Skills
CuriosityDecision MakingCollaborationProblem SolvingCommunication
Tools & Technologies
DagsterRayAPIsData StoresData Evaluation Tools
Industry Keywords
Patent DataDocument DataReal-World DataData QualityData Sources
Tech Stack
Tools & technologiesPythonRay
About the role
Key responsibilities & impact- Learn what the data means, identify shortcomings, and model relationships among patents, research papers, and companies
- Build harnesses and evaluations that allow agents to fix data problems and assess result quality
- Build and run Python data pipelines using Dagster
- Monitor pipelines with data quality checks
- Deliver source data into the stores and APIs used by the product
- Collaborate with product teams to define required data and formats
- Work with the ML team on machine-learning processing steps
- Explore and evaluate new data sources, including direct patent-office sources
- Investigate failing or inadequate data sources and identify alternatives
- Collaborate with a three-person data engineering team, engineering teams, and the wider company
Requirements
What you’ll need- Experience building and running production pipelines on large, noisy real-world data, or working with such data in statistics or ML
- Comfortable making decisions when there is no fixed definition of correct
- Experience building statistical evaluations and measurable targets into work
- Ability to build agent harnesses for data processing systems
- Curiosity about the real world behind data and how to represent it in processing systems
- Ability to determine what correct data looks like when surrounding information is imperfect
- Ability to work directly with data users inside and outside engineering
- Experience with patent or other document data is advantageous but not required
- Experience with distributed processing such as Ray is advantageous but not required
- Experience running ML models over large datasets is advantageous but not required
- Ability to learn the stated data stack; knowledge of all technologies is not required
- Submit a CV and a half-page response to: “What's the most interesting dataset you've worked with, and what did it take before you could trust it?”
Benefits
Comp & perks- Equity on top of salary
- Hybrid work arrangement
- Opportunity to experiment and help shape how the data team works with AI agents
- Considerable room to experiment
- Work with a distributed data engineering and broader IPRally team