Data Quality Engineer - Senior Associate - Public Sector

Company description Tremend is the newest global software engineering hub for Publicis Sapient. For over 20 years, the company has been infusing its advanced technical expertise into complex and innovative solutions that meet today's digital transformation needs and pave the way for a better and smarter future. By joining forces with Publicis Sapient we're accelerating the impact, providing a good mix of talented engineers, technology, continuous improvement, innovation, and R&D. Here, you'll have the opportunity to unleash your potential, powering up advanced software solutions for some of the world's most iconic brands. Embrace your passion for technology, creativity, and continuous improvement, and join us in making a difference through engineering. Overview We are looking for a Senior Data Quality Engineer to join our data engineering and quality assurance team. The role is focused on validating complex data platforms, data pipelines, BI reports, and analytical datasets across cloud and data warehouse environments. The ideal candidate has strong SQL skills, hands-on experience with data reconciliation, ETL/data pipeline testing, and the ability to create automated data quality checks using Python, PySpark, or another programming language. This is a senior role for someone who can work independently with data engineers, business analysts, product owners, and client stakeholders to ensure data accuracy, completeness, consistency, and reliability from source systems through transformation layers and final reporting outputs. Responsibilities Responsibilities Design and execute data quality testing strategies for data pipelines, data warehouses, data lakes, and reporting platforms. Validate source-to-target and source-to-report data flows to ensure accuracy, completeness, consistency, and timeliness. Create, maintain, and execute SQL-based validation scripts across multiple data layers. Build automated data quality checks using Python, PySpark, or similar scripting/programming languages. Test ETL/ELT pipelines, transformation logic, data models, and business rules. Validate BI reports and dashboards against source data, business requirements, and transformation rules. Investigate data discrepancies, identify root causes, and collaborate with engineering teams to resolve defects. Review data mappings, technical specifications, business rules, and reporting requirements. Support testing across environments such as DEV, QA, UAT, and PROD. Work with data engineers to validate code changes, pipeline executions, and data loads. Track defects and testing progress using tools such as Jira, ALM, ServiceNow, or similar. Contribute to test planning, test documentation, data quality reporting, and release readiness assessments. Support incident analysis and production data issue investigation when needed. Collaborate directly with business users and stakeholders to clarify expected data behavior and validate fixes. Skills and Experience Senior-level experience in Data Quality Engineering, Data Testing, ETL Testing, Data Warehouse Testing, or similar roles. Strong hands-on experience with SQL for data validation, reconciliation, profiling, and defect investigation. Experience testing data pipelines, ETL/ELT flows, data transformations, and reporting datasets. Good understanding of data warehouse concepts, data marts, staging layers, bronze/silver/gold layers, and source-to-target validation. Experience with at least one automation or scripting language, preferably: Python / PySpark / Java / JavaScript/TypeScript / Scala or another relevant language used for automated data checks. Ability to create reusable automated checks for data completeness, accuracy, duplication, null validation, referential integrity, and business rule validation. Experience validating BI reports or dashboards, preferably with tools such as Power BI, Tableau, Looker, or similar. Experience working with defect management and collaboration tools such as Jira, ALM, ServiceNow, Azure DevOps, or similar. Strong analytical mindset and ability to investigate complex data issues independently. Good communication skills and ability to explain data quality findings to both technical and non-technical stakeholders. Experience working in Agile delivery environments. Set yourself apart with: Experience with Microsoft Fabric, Azure Data Factory, Databricks, Google Cloud, BigQuery, Snowflake, or similar platforms. Experience with PySpark notebooks and distributed data processing. Experience with DBeaver, Teradata, SQL Server, PostgreSQL, Oracle, or similar databases. Experience with ETL tools such as IBM InfoSphere DataStage, Informatica, Talend, dbt, or similar. Experience with cloud data platforms on Azure, GCP, or AWS. Experience with CI/CD integration for data quality tests. Experience with data observability, metadata validation, lineage checks, or automated monitoring. Experience in financial services, public sector, telecom, banking, or regulated environments. Familiarity with test automation frameworks and quality engineering practices. Understanding of production support, incident management, and release validation processes. Additional information Besides an exciting job in a tremendous team, here's what you can expect: A fast-paced tech environment Continuous growth & learning Open feedback culture Room for own initiative & ideas Transparency about results & strategy Recognition & reward for hard work Working with a flexible schedule Medical subscription Meal tickets Extra vacation days - starting with 25 vacation days Many other perks

Responsibilities Design and execute data quality testing strategies for data pipelines, data warehouses, data lakes, and reporting platforms. Validate source-to-target and source-to-report data flows to ensure accuracy, completeness, consistency, and timeliness. Create, maintain, and execute SQL-based validation scripts across multiple data layers. Build automated data quality checks using Python, PySpark, or similar scripting/programming languages. Test ETL/ELT pipelines, transformation logic, data models, and business rules. Validate BI reports and dashboards against source data, business requirements, and transformation rules. Investigate data discrepancies, identify root causes, and collaborate with engineering teams to resolve defects. Review data mappings, technical specifications, business rules, and reporting requirements. Support testing across environments such as DEV, QA, UAT, and PROD. Work with data engineers to validate code changes, pipeline executions, and data loads. Track defects and testing progress using tools such as Jira, ALM, ServiceNow, or similar. Contribute to test planning, test documentation, data quality reporting, and release readiness assessments. Support incident analysis and production data issue investigation when needed. Collaborate directly with business users and stakeholders to clarify expected data behavior and validate fixes. Skills and Experience Senior-level experience in Data Quality Engineering, Data Testing, ETL Testing, Data Warehouse Testing, or similar roles. Strong hands-on experience with SQL for data validation, reconciliation, profiling, and defect investigation. Experience testing data pipelines, ETL/ELT flows, data transformations, and reporting datasets. Good understanding of data warehouse concepts, data marts, staging layers, bronze/silver/gold layers, and source-to-target validation. Experience with at least one automation or scripting language, preferably: Python / PySpark / Java / JavaScript/TypeScript / Scala or another relevant language used for automated data checks. Ability to create reusable automated checks for data completeness, accuracy, duplication, null validation, referential integrity, and business rule validation. Experience validating BI reports or dashboards, preferably with tools such as Power BI, Tableau, Looker, or similar. Experience working with defect management and collaboration tools such as Jira, ALM, ServiceNow, Azure DevOps, or similar. Strong analytical mindset and ability to investigate complex data issues independently. Good communication skills and ability to explain data quality findings to both technical and non-technical stakeholders. Experience working in Agile delivery environments. Set yourself apart with: Experience with Microsoft Fabric, Azure Data Factory, Databricks, Google Cloud, BigQuery, Snowflake, or similar platforms. Experience with PySpark notebooks and distributed data processing. Experience with DBeaver, Teradata, SQL Server, PostgreSQL, Oracle, or similar databases. Experience with ETL tools such as IBM InfoSphere DataStage, Informatica, Talend, dbt, or similar. Experience with cloud data platforms on Azure, GCP, or AWS. Experience with CI/CD integration for data quality tests. Experience with data observability, metadata validation, lineage checks, or automated monitoring. Experience in financial services, public sector, telecom, banking, or regulated environments. Familiarity with test automation frameworks and quality engineering practices. Understanding of production support, incident management, and release validation processes.