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Since 2014, Matic has changed the landscape of insurance technology by integrating home insurance within the homeownership experience. Today, our digital insurance marketplace has over 30 A-rated home and auto carriers and distribution partners in industries ranging from mortgage originations and servicing to banking, real estate and personal finance.
23 октября 2020

Data QA Engineer (Product) (вакансия неактивна)

Львов

Every day our insurance products are generated thousands of records which are then transformed into analytical insights and drive business outcomes. Decisions made for the development of our company depend on the quality of these data, so we are looking for a Data Quality specialist who will help us keep our data accurate. Our data team consists of 7 people (5 Data Analysts, Data Engineer and Data Scientist) and despite the difference in location and time zones, we support and help each other.

Day to day duties:

— Analyzing and testing of data for our data warehouse
— Collaborate with other data professionals, engineers, and business operation experts to understand needs
— Write and execute test cases, create automation scripts, evaluate compared to expected results as needed, and communicating relevant insights to the team
— Develop high-level test strategies and plans for all projects by analyzing project documentation
— Produce the appropriate documentation and test evidence

Bonuses:

— We do not limit the choice of tools and welcome experiments
— Opportunity to try yourself in different parts of working with data, we support the development of skills

Must have skills:

— Proficient in SQL
— Experience developing test plans
— Demonstrated ability to implement and maintain QA processes
— A passion for technology — we are looking for someone who is keen to leverage their existing skills and seek out new skills and solutions
— Upper Intermediate+ level of English (our Data Team is distributed)
— Strongly details oriented

Nice to have:

— Experience with business intelligence tools (Metabase, Tableau)
— Experience with Snowflake

Tools we use:

— Snowflake as a Data Lake storage
— Products Data is collected in the Data Lake using ETL (Fivetran, Stitch), and User engagement captured via Segment
— Data Aggregated by DBT (SQL models)
— Data Visualized using Tableau and Metabase

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