Ajax Systems is an international technology company and the largest security systems manufacturer in Europe. Over 4.5 million end users and 330 thousand PRO users in more than 180 countries trust Ajax products. The company offers comprehensive solutions for protecting residential and commercial properties of various scales. Ajax's portfolio now includes 280 devices for intrusion protection, video surveillance, fire safety, and comfort and automation. Every year we demonstrate multiple growth in both team size and the number of users worldwide.
Ajax Systems has a full-cycle production: from idea generation and prototyping to mass production and worldwide sales. Each department fully covers a specific operational need of the business. The company's specialists are involved at all stages: from the production line in Ukraine to the logistics center in Poland, from where products are shipped to distributors around the world.
We are currently looking for a Data QA Engineer (Middle) for the Data Warehouse & Analytics team, who will help build a systematic approach to data testing and ensure data quality at all stages of processing, from sources to final analytical data marts. Our team works on developing the corporate data platform based on Databricks, integrating data from SAP, 1C and other systems, preparing master data for SAP implementation at international production sites, and automating management and analytical reporting. This is an opportunity to join the building of Data QA processes practically from scratch, influence testing approaches, and develop automation of data quality checks.
Main responsibilities: Building and developing data testing processes within the Data Warehouse & Analytics team.
Testing ETL/ELT processes and verifying the correctness of data transfer and transformation from SAP, 1C and other sources to Databricks.
Verifying that data complies with business requirements at all stages of processing: from source systems to final analytical data marts.
Performing Data Validation and Source-to-Target Reconciliation to ensure data accuracy, completeness and consistency.
Verifying the business logic of transformations, processing rules and data enrichment.
Identifying duplicates, missing records, incorrect values and other data anomalies.
Developing and maintaining test cases, test scenarios and data quality check rules.
Building and developing automated data testing, including regression checks.
Verifying data correctness after changes in data pipelines, data structures and business logic.
Participating in testing master data preparation processes for SAP implementation.
Analyzing the causes of data discrepancies and interacting with Data Engineers, Business Analysts and related teams to resolve them.
Participating in improving Data Quality approaches and increasing the reliability of data processing.
What we expect: At least 2 years of commercial experience in Data QA, ETL QA, DWH QA or Software QA with a significant focus on data testing.
Confident SQL skills: independently writing queries, working with JOINs, CTEs, filtering, aggregation and selections from multiple tables.
Knowledge of Python and understanding of how it can be used for testing and automating data checks.
Understanding of Quality Engineering principles and practical approaches to building testing processes.
Understanding of ETL/ELT processes, Data Warehouse architecture and the data lifecycle.
Knowledge of Data Quality, Data Validation, Data Reconciliation and Data Governance principles.
Experience verifying the correctness of data transformations and the compliance of results with business requirements.
Understanding of regression testing principles and test automation.
Well-developed analytical skills, the ability to independently understand complex business logic and find the causes of data problems.
Proactivity, systematic thinking, constructive communication and the ability to organize work under dynamic priorities.
English at Intermediate level or higher for working with documentation and communicating with international teams.
As a plus: Practical experience in automating Data Quality / Data Validation tests.
Experience with Databricks, PySpark, Spark.
Experience with Python-based testing frameworks, pytest, Great Expectations, dbt tests, Soda or similar tools.
Experience testing SAP integrations with DWH or other corporate data platforms.
Experience building testing processes from scratch.
Understanding of CI/CD and experience integrating automated tests into development processes.
Experience working with large data volumes and multi-source integrations.
Experience with BI solutions, including Power BI.
What we offer: Participation in large-scale international projects to develop the corporate data platform.
The opportunity to build and develop the Data QA direction, influencing tools and testing approaches.
Work with a modern technology stack: Databricks, Python, SQL, PySpark.
Collaboration with Data Engineers, SAP specialists and business analysts.
Opportunities for professional growth at the intersection of Quality Engineering, Data Engineering and Business Analytics.
Work in one of our comfortable offices. The office is equipped with a generator and a shelter. Official employment with stable pay and all the guarantees of a Diia City resident. Flexible schedule. Training compensation. English classes. Medical insurance. Ajax security system kit (participation in the beta test project: receiving and testing new gadgets before their release). A driving team and a zero bullshit culture.
Join us to make Made in Ukraine a synonym for reliability!