Data Quality Analyst / Data Tester (Bulgaria/Romania)

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.

The project involves migration of legacy and TMS-related data into Google BigQuery as part of a broader data platform modernization initiative. The team is responsible for designing ingestion frameworks, implementing transformation pipelines, ensuring data quality, and delivering production-grade datasets for analytical consumption.

Key Technologies
Required: SQL, Data Reconciliation, Data Quality Testing, ETL/ELT Validation
Preferred: Google BigQuery, Python, BI/Reporting Testing, APIs, Data Profiling
Nice to Have: Looker, dbt, Airflow/Composer, GCP, Data Observability, Data Governance

You will be:

• Strong SQL skills, including complex queries, joins, aggregations, window functions, and data comparisons
  • Hands-on experience validating data across different systems, databases, or reporting platforms
  • Proven experience with data quality testing, reconciliation, and validation of migrated data
  • Ability to analyse discrepancies between source and target datasets and identify their root causes
  • Experience validating ETL/ELT pipelines, including completeness, accuracy, consistency, and transformation logic
  • Good understanding of data models, relationships, schemas, and data dependencies
  • Experience testing structured and semi-structured data
  • Ability to translate business and reporting requirements into test scenarios and validation rules
  • Experience defining and executing data quality checks, including:
  o record counts and completeness
  o null and duplicate checks
  o referential integrity
  o field-level reconciliation
  o aggregation and KPI validation
  o business rule validation
  o historical data consistency
  • Ability to distinguish between source data issues, transformation defects, and reporting/visualisation issues
  • Experience documenting defects and working closely with Data Engineers to investigate and resolve data discrepancies
  • Good analytical and problem-solving skills
  • Experience working with Git-based development workflows and Agile delivery teams

Your profile:

• Your experience:
  • Hands-on experience with Google BigQuery
  • Experience testing data migrations or reporting platform migrations
  • Experience validating dashboards, reports, KPIs, and analytical datasets
  • Understanding of ETL/ELT concepts and modern cloud data architectures
  • Experience with automated or semi-automated data validation using SQL and/or Python
  • Basic Python skills for data analysis, comparison, or test automation
  • Experience with data profiling and identifying anomalies in large datasets
  • Understanding of data quality dimensions such as accuracy, completeness, consistency, uniqueness, validity, and timeliness
  • Experience working with APIs and validating externally sourced data
  • Understanding of data lineage and the ability to trace data from source through transformations to the reporting layer
  • Experience with defect management and test documentation
  • Ability to work directly with business stakeholders to clarify expected reporting results and business rules
  
  

• • Practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.

  • Work from the European Union region and a work permit are required.

Nice to have:

• • Hands-on experience with Looker or another BI/reporting platform
  • Experience with GCP Dataform tests or similar data testing frameworks (eg. dbt tests)
  • Experience in Retail or eCommerce
  • Experience with reconciliation of sales, stock, product, customer, or transactional data
  • Familiarity with Airflow / Cloud Composer
  • Experience with data observability or data quality tools
  • Understanding of CI/CD practices for data pipelines and automated data tests
  • Experience with Google Cloud Platform and its data services
  • Familiarity with Data Governance, metadata management, and data cataloguing
  • Experience preparing or validating datasets used for Analytics, Data Science, or Machine Learning

• Experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work.

• Interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches.

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision