{"id":3513716,"url":"https://alion.io/job/algoworks-qa-engineer-etl","title":"QA Engineer - ETL","company":{"id":2091826,"name":"Algoworks","domain":"algoworks.com","url":"https://alion.io/company/algoworks-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"QA","role_family":"QA","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":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":2000000,"max":2500000,"currency":"INR","period":"year","gross":null,"usd_annual":26478},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Redshift","optional":true},{"name":"Databricks","optional":true},{"name":"dbt","optional":true},{"name":"FHIR","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Great Expectations","optional":true},{"name":"HL7","optional":true},{"name":"Prefect","optional":true},{"name":"Snowflake","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-30T07:21:11Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-10-31T02:48:23Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Role Overview\nWe are seeking an ETL QA Data Engineer to ensure the accuracy, completeness, and reliability of data pipelines and analytics outputs across a high-volume, event-driven domain (e.g., emergency response, public safety, and healthcare operations). You will validate data ingestion and transformation logic, reconcile records between source and target systems, detect anomalies, and help ensure reporting and dashboards reflect trustworthy data.\nKey Responsibilities\nData Validation & Reconciliation \nPerform source-to-target reconciliation for event/transaction-style datasets across ingestion and curated layers. \nValidate completeness, timeliness, and consistency of daily/near-real-time ingestion pipelines. \nIdentify and investigate data anomalies (missing records, spikes/drops, duplicates, out-of-range values, schema drift). \nValidate correctness of KPIs and metrics used in dashboards and reports (e.g., volumes, rates, turnaround times, error trends). \nEnsure referential integrity across key entities (e.g., events/encounters, units/resources, locations, agencies/sites). \nPartner with engineering teams to debug discrepancies, confirm root causes, and validate fixes. \nAutomation & Framework Development \nBuild and maintain automated data validation frameworks using Python (PyTest/Unittest) and data quality libraries/tools. \nDevelop reusable validation assets: \nreconciliation templates \ndata quality rules (nulls, ranges, uniqueness, referential integrity) \nregression suites for critical pipelines \nIntegrate validation into CI/CD and orchestration workflows (e.g., validation gates per pipeline run). \nProduce validation artifacts (test reports, defect summaries, data quality scorecards) for ongoing monitoring. \nAI-Assisted Validation (with Human Verification) \nUse AI tools (LLMs, Qodo, etc.) to accelerate: \nvalidation query generation \nreconciliation logic \ntest case creation (including edge-case discovery) \nApply AI-assisted techniques for anomaly detection ideas, boundary scenarios, and coverage expansion. \nValidate all AI-generated outputs against actual data and documented business rules before adoption. \nRequired Qualifications\nStrong SQL skills for reconciliation and validation (joins, aggregation, window functions, profiling). \nStrong hands-on Python experience for test automation and data validation (not just scripting). \nExperience in ETL/ELT QA, data testing, analytics validation, or data quality engineering. \nUnderstanding of event-driven / transaction-based systems and common pipeline failure patterns (late-arriving data, corrections, duplicates). \nExperience validating large datasets and reporting layers with high reliability expectations. \nPreferred Qualifications\nExperience with data quality tools such as: \n Great Expectations (preferred) \ncustom Python validation frameworks \ndbt tests / similar validation frameworks \nFamiliarity with cloud data platforms and modern data stacks (any of): \nSnowflake / BigQuery / Redshift \nDatabricks / Spark \nAirflow / ADF / Prefect \nExperience with sensitive data handling practices (privacy/security considerations). \nExposure to interoperability/integration patterns (APIs, HL7/FHIR, EDI, batch feeds)-as applicable to the environment. \nWhat Success Looks Like\nAutomated validation coverage for critical pipelines (freshness, completeness, reconciliation). \nEarly detection of anomalies and measurable reduction in downstream reporting issues. \nRepeatable, documented validation rules aligned to business definitions and metrics. \nEfficient collaboration with engineering teams to shorten incident triage and fix verification cycles.","description_format":"text","description_chars":3637,"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":["IT Consulting & Digital Transformation","Custom Software Development"],"lifecycle":[{"event":"open","at":"2026-10-30T09:19:23Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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