Automation Engineer

Location: Bengaluru

- Automation Engineering SDE2

- Experience:

2–4 years
- Salary Range: 8

LPA – 15

LPA
- 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 (Git Hub Actions, Jenkins, Git Lab 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, Rest Assured, pytest-httpx) to validate data interfaces and
service contracts.

▸ Understanding of cloud data environments (AWS S3, Azure Data Lake, GCP Big Query) and how data
flows through them.
▸ Exposure to version control best practices, code review culture, and engineering documentation.