Quick take: Axios is a new digital media company that gets you smarter, faster with news that matters. Our Engineering team is hiring a Data Quality Engineer!

Why it matters:  The quality team’s mission at Axios is to ensure we deliver exceptional technology for our readers and clients every day. In this role you will be collaborating with multi-functional teams such as data engineering, data science and product teams to test, validate and monitor our data science products, data platforms and the data flowing through them. Quality engineers integrate tightly into our cross-functional teams to refine requirements for our products and ensure the systems delivered line up.

Go deeper: The Data Quality Engineer’s responsibility is to ensure Axios is shipping better products consistently. Responsibilities of this role include:

• Collaborating with technical leads, data engineers, data scientists, analysts, product managers and team to define and refine requirements
• Providing feedback to the team on the potential risks and mitigation tactics of their intended approach
• Coaching the team to make quality a central part of their practice
• Utilizing both manual and automated testing strategies
• Managing bugs, including identification, logging, tracking and triaging
• Working with the team to manage risk and reduce it to an acceptable level
• Working with engineers to implement test automation
• Writing and executing test plans

The details: Ideal candidates will embody an entrepreneurial spirit, a passion for Axios’ mission and have the following skills and attributes:

• Experience in Software Quality Assurance, Quality Engineering, or as an SDET
• Advanced programming skills in Python and SQL
• Experience testing in an Agile environment
• Experience detecting and reporting data quality issues
• Experience with test/virtual environments, deployment tools, and the command line
• Excellent written and verbal communication skills
• Exceptionally collaborative
• Obsessive attention to detail
• A belief in and commitment to Axios’ diversity, equity, and inclusion values

We’ll be even more excited if you have:

• Familiarity with Data Science methodologies, including supervised and unsupervised machine learning problems
• Experience working with data pipelines and familiarity with modern data engineering frameworks: Kafka, PySpark, Circle, AWS, Looker, Airflow, SQL/noSQL databases
• Experience testing services and working with Docker containers
• Experience working in a Continuous Integration environment

Don’t forget:

• Competitive salary
• Health insurance (100% paid for individuals, 75% for families)
• Primary caregiver 12-week paid leave
• 401K
• Generous vacation policy, plus company holidays
• Company equity
• Commuter and cell phone benefit
• A commitment to an open, inclusive, and diverse work culture
• Annual learning and development stipend

Additional pandemic-related benefits:

• One mental health day per month
• $100 monthly work-from-home stipend
• Company-sponsored access to Ginger coaching and mental health support 
• OneMedical membership, including tele-health services 
• Increased work flexibility for parents and caretakers 
• Access to the Axios “Family Fund”, which was created to allow employees to request financial support when facing financial hardship or emergencies 
• Class pass discount
• Virtual company-sponsored social events

Equal Opportunity Employer Statement

Axios is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, age, gender identity, gender expression, veteran status, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.

This policy applies to all employment practices within our organization, including hiring, recruiting, promotion, termination, layoff, recall, leave of absence, compensation, benefits, training, and apprenticeship. Axios makes hiring decisions based solely on qualifications, merit, and business needs at the time.