Automation Test Lead – Data & Platform Engineering

🕒 6 days ago

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Virtusa

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1996

💼 Consulting

🏢 Enterprise

🤖 Artificial Intelligence

💰 $108M Post-IPO Equity - Virtusa on 2017-05

Consulting • Enterprise • Artificial Intelligence

Virtusa is a global technology services and consulting company that helps enterprises reimagine their business through digital engineering, AI-driven and domain-focused solutions. It offers consulting, design, engineering, automation, analytics and optimization services, and partners with organizations across banking & financial services, communications, healthcare, insurance, retail, manufacturing and other industries to deliver enterprise-scale technology and AI solutions. Virtusa emphasizes AI services, domain-driven solutions for the AI enterprise, and end-to-end digital transformation.

📋 Description

• Test Automation Architecture & Strategy: Own a scalable, modular, metadata-driven framework covering data pipelines (batch/streaming), APIs/backend services, and end-to-end data product validation; enable plug-and-play components, parallel execution, environment isolation, deterministic runs • Data Testing Framework Engineering: SQL-based assertions/reconciliation, schema validation, data contracts, lineage/freshness validation, config-driven test definitions (YAML/JSON) • Destructive Testing: Schema drift, backward incompatibility, late-arriving data, partial failures, duplicate/missing/out-of-order events, stress, concurrency, retries, DLQ handling, backpressure • ETL/Streaming Validation at Scale: Row/aggregate/hash-based reconciliation, incremental/backfill validation, delivery semantics validation, window/time-based correctness • Data Quality & Observability: Integrate/extend Great Expectations or Soda; custom validations for accuracy, completeness, uniqueness, timeliness; quality dashboards • CI/CD & DataOps Enforcement: Pre-merge gates, release blockers, selective/parallel test execution, GitHub Actions/Jenkins integration • Test Data Management: Synthetic data generation, masking/anonymization, deterministic datasets, edge case simulation • Performance & Reliability Testing: Pipeline/query benchmarks, concurrency/stress testing, data skew analysis, cost/time optimization • Security & Compliance: PII/PHI exposure checks, encryption/access control, retention, audit requirements; support GxP/SOX/ISO frameworks • Cross-Functional Quality Leadership: Work with data engineers, platform teams, architects; mentor engineers • Incident Analysis & Prevention: Root-cause analysis of production data issues, reduce flaky tests

🎯 Requirements

• Strong Python (test frameworks, libraries, CLI tools); advanced SQL • Hands-on DBT, Airflow, Snowflake or similar; ETL/ELT and data modeling • Great Expectations/Soda; lineage and catalog systems • Kafka/Kinesis testing; delivery semantics • Git workflows; CI/CD (Jenkins, GitHub Actions) • AWS; IaC (Terraform/CloudFormation)