Automation Engineer

- Automation Engineering SDE2 - Experience: 2–4 years - Salary Range: 8LPA – 15LPA - Key Skills: Automation tools, Data pipelines, SQL, Python, and LLM-based validation. - Work Arrangement: Hybrid. What You'll Do Data Validation & Framework Development ▸ Design, build, and maintain a scalable data validation framework to verify the correctness and integrity of data pipelines end-to-end. ▸ Write automated tests that validate data ingestion, transformation, aggregation, and output layers across the Zynix data platform. ▸ Define and implement data quality rules covering completeness, accuracy, consistency, timeliness, and schema conformance. ▸ Develop reusable validation utilities and libraries that other engineers can plug into CI/CD pipelines with minimal friction. AI & Model Output Testing ▸ Build test harnesses and validation suites to evaluate AI model outputs — including predictions, agent decisions, and clinical recommendations — against expected baselines. ▸ Detect and flag regressions, data drift, or unexpected model behavior through systematic automated checks. ▸ Collaborate with Data Scientists and ML Engineers to define testable acceptance criteria for AI features. ▸ Contribute to the development of golden datasets and test fixtures that reflect real-world clinical data scenarios.

Test Automation & CI/CD Integration ▸ Develop and execute automated test suites (functional, regression, integration, and performance) for data-intensive workflows. ▸ Integrate test automation into CI/CD pipelines to ensure continuous validation on every code or data change. ▸ Identify gaps in test coverage, prioritize accordingly, and proactively raise risks before they reach production. ▸ Maintain test documentation, reports, and dashboards to give engineering teams clear visibility into data health. Independent Ownership & Collaboration ▸ Work independently to scope, plan, and deliver validation solutions — from requirement gathering to framework deployment. ▸ Partner with Data Engineers, Backend Engineers, and Product Managers to understand data contracts and business rules. ▸ Participate in architecture and design reviews with a quality and validation lens. ▸ Evangelize a data quality mindset across the engineering team through documentation, standards, and knowledge sharing. What You Bring Required Qualifications ▸ 2–4 years of experience in software automation testing, QA engineering, or a closely related discipline. ▸ Solid understanding of automation testing principles — test design, test pyramid, boundary conditions, regression strategy, and defect lifecycle. ▸ Proficiency in at least one scripting/programming language (Python strongly preferred) for writing test scripts and automation frameworks. ▸ Hands-on experience or strong conceptual understanding of data platforms and big data processing (Databricks, Spark, PySpark, or similar). ▸ Familiarity with SQL and the ability to write complex queries for data validation and reconciliation. ▸ Exposure to CI/CD tools and practices (GitHub Actions, Jenkins, GitLab CI, or equivalent). ▸ Strong problem-solving skills and the ability to work independently — define the problem, design the solution, and deliver it. ▸ Good communication skills; able to document findings clearly and raise risks effectively.

Preferred Qualifications ▸ Direct experience with Databricks — notebooks, Delta Lake, Unity Catalog, or Databricks Workflows. ▸ Exposure to AI/ML validation concepts — model evaluation metrics, regression testing for ML models, or prompt/output validation for LLM-based systems. ▸ Experience with data quality tools such as Great Expectations, dbt tests, Soda, or custom-built validation frameworks. ▸ Familiarity with healthcare data standards (HL7, FHIR, claims data, ADT feeds) — a plus, not a must. ▸ Experience with API testing tools (Postman, RestAssured, pytest-httpx) to validate data interfaces and service contracts. ▸ Understanding of cloud data environments (AWS S3, Azure Data Lake, GCP BigQuery) and how data flows through them. ▸ Exposure to version control best practices, code review culture, and engineering documentation.