{"id":2411157,"url":"https://alion.io/job/algoleap-data-quality-engineer-data-qe","title":"Data Quality Engineer (Data QE)","company":{"id":3800168,"name":"Algoleap","domain":"algoleap.com","url":"https://alion.io/company/algoleap-technologies","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16000,"max_usd":44000,"period":"year","method":"role_country_seniority_unknown","sample_n":171},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google BigQuery","optional":false},{"name":"MS SQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-13T01:54:01Z","employer_posted_date":null,"last_verified_at":"2026-10-13T01:54:01Z","board_verified":false,"closed_at":null,"days_open":19,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":19},"description":"Job Summary\n\nWe are seeking an experienced Data Quality Engineer (Data QE) to ensure the quality, accuracy, reliability, and integrity of data across enterprise data platforms. The ideal candidate will have strong experience in Data Warehousing, Medallion Architecture, ETL/ELT testing, Data Governance, Data Quality frameworks, Data Lineage, and test automation. This role will be responsible for validating data pipelines from multiple source systems such as CDP, GA4, BigQuery, and Azure Blob Storage into Snowflake, and ensuring accurate reporting in Power BI.\n\nRequired Experience\n\n__ years of experience in Data Quality Engineering, Data Warehouse Testing, or Data Validation.\nStrong hands-on experience with PostgreSQL, Microsoft SQL Server, Snowflake and BigQuery.\nExperience validating enterprise Data Warehouse solutions and Medallion Architecture implementations.\nProven experience performing Source-to-Target validation from CDP, GA4, BigQuery, Blob Storage, PostgreSQL, and MSSQL into Snowflake.\nExperience in validating Power BI reports, dashboards, semantic models, and KPIs.\nStrong understanding of ETL/ELT data processing, data transformations, and data lineage.\nExperience working with Data Governance, Data Quality, Metadata Management, Data Lineage, Data Dictionary, and Data Catalog solutions.\nExperience with Data Quality tools such as Soda or equivalent.\nExperience designing, executing, and automating test cases for large-scale data platforms using SQL, Python, and PySpark.\nExperience integrating automated testing into CI/CD pipelines using Azure DevOps or GitHub Actions.\n\nKey Responsibilities\n\nData Validation & Testing\n\nPerform end-to-end data validation across the data lifecycle, including ingestion, transformation, storage, and reporting.\nValidate data movement from source systems including:\nCustomer Data Platforms (CDP)\nGoogle Analytics 4 (GA4)\nGoogle BigQuery\nAzure Blob Storage\nPostgreSQL\nMSSQL\nSnowflake\nPerform comprehensive Source-to-Target (S2T) data validation between source systems and Snowflake.\nValidate data accuracy, completeness, consistency, uniqueness, and timeliness across datasets.\nExecute data reconciliation and data profiling activities.\nConduct source-to-target validation across databases, data lakes, and data warehouses.\nValidate schema changes, constraints, indexes, stored procedures, functions, triggers, and views.\n\nMedallion Architecture Validation\n\nValidate data across Bronze, Silver, and Gold layers within the Medallion Architecture.\nVerify data transformations, cleansing, aggregations, and business rules between layers.\nEnsure data quality controls are enforced throughout the Medallion framework.\n\nETL/ELT Testing\n\nTest and validate ETL/ELT pipelines and data transformations.\nValidate business mappings, transformation logic, and data lineage.\nVerify incremental and full-load processes.\nPerform regression, integration, functional, and end-to-end testing of data pipelines.\nIdentify and troubleshoot data discrepancies across source and target systems.\n\nSnowflake Data Validation\n\nValidate data ingestion, transformations, and storage within Snowflake.\nPerform large-scale data validation using SQL and automation scripts.\nValidate Snowflake views, tables, materialized views, stored procedures, and data sharing mechanisms.\n\nPower BI Testing\n\nValidate datasets, data models, measures, KPIs, and dashboards.\nEnsure Power BI reports accurately reflect Snowflake data.\nVerify report-level calculations, filters, row-level security, and business metrics.\nPerform end-to-end testing from source systems through Snowflake into Power BI.\n\nData Governance & Data Management\n\nValidate implementation of Data Governance policies and standards.\nEnsure compliance with enterprise data quality and governance requirements.\nValidate and maintain:\nData Lineage\nData Dictionary\nMetadata Management\nBusiness Glossary\nData Catalogs\nCollaborate with Data Governance and Business teams to improve data quality processes.\n\nData Quality Frameworks\n\nImplement and execute data quality checks and monitoring solutions.\nDefine and monitor Data Quality KPIs and metrics.\nDevelop and automate validation rules for:\nCompleteness\nAccuracy\nConsistency\nUniqueness\nValidity\nTimeliness\nSupport root cause analysis and remediation of data quality issues.\n\nTest Automation\n\nDevelop automated data validation frameworks and reusable test suites.\nAutomate source-to-target reconciliation and regression testing.\nIntegrate automated data quality tests into CI/CD pipelines.\nBuild automated validation using SQL, Python, PySpark, and Data Quality tools.\n\nRequired Technical Skills\n\nDatabases & Data Warehousing\n\nPostgreSQL\nMicrosoft SQL Server (MSSQL)\nSnowflake\nBigQuery\nData Warehouse Testing\nData Modeling Concepts\nStar Schema & Snowflake Schema\nSlowly Changing Dimensions (SCD)\nFact & Dimension Validation\nCDC Validation\nData Migration Testing\n\nSQL Expertise\n\nAdvanced SQL Query Writing\nComplex Joins\nWindow Functions\nAggregations\nQuery Optimization\nData Reconciliation Queries\nData Profiling and Data Analysis\n\nModern Data Platform Validation\n\nMedallion Architecture Validation (Bronze, Silver, Gold)\nSource-to-Target Testing\nETL/ELT Testing\nData Lake & Data Warehouse Validation\nPower BI Report & Dashboard Validation","description_format":"text","description_chars":5262,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Custom Software Development","AI Consulting & Integration"],"lifecycle":[{"event":"open","at":"2026-10-13T02:09:00Z"}],"visa":[],"liveness":{"score":49,"band":"ok","label":"Likely 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