Position: Senior Data Quality Engineer
Company: Nixwood
Experience required: 3y
English level: upper

The company is a provider of high-quality business services in such areas as IT, Procurement, HR, Product Lifecycle Services, Financial Reporting and Analytics, and Real Estate and Facility Services.

Responsibilities
Design, lead, and manage a data implementation strategy
Manage and maintain data assets in alignment with data management processes
Ensure data governance and compliance through appropriate processes
Participate in mapping business data sources to appropriate repositories of data
Identify new critical data elements and metadata
Coordinate and ensure the implementation of data asset management standards and policies in close collaboration with the providers of the data
Accountable for data discrepancies and mitigation, data availability and consistency
Drive compliance risk mitigations for regional data privacy and security
Participate in mapping business data needs to appropriate sources of data; provide guidance and coordinate/resolve data issues
Collaborate with internal data governance teams and ensure the implementation of data governance and data asset management standards and policies

Requirements
3+ years of experience in Data Quality Engineering
Advanced Data Analysis using T-SQL, Python (familiarity with Pandas); should be able to write complex scripts
Expert at doing Data Profiling and documenting the results
Expert at identifying common data issues related to data types, formats, missing values, duplicate data, etc.
Concrete experience working with various file formats more specifically CSV, Parquet, Any other Delimited, XML, JSON
Expert at working with relational tables in SQL Server Management Studio
Good understanding of time series and historical data. Good understanding
Good understanding of OLTP (Normalized tables in 3NF) and OLAP (Star Schema and Snowflake schema dimension and fact tables) systems
Familiarity with various encodings like ASCII and UNICODE and their implications on data quality
Good understanding of QA and Testing best practices
Be able to create test plans and test cases to do end-to-end testing of data pipelines, and appropriately identify data quality issues, and document them for bug fixes; once the bugs are fixed should be able to go back and do regression testing
Good with designing test harness for automated testing
Ideally good exposure to Azure DevOps
Good understanding of various ETL Tools especially ADF and Databricks
Proficient English (written and spoken) B2