Data Quality Analyst

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