Do you want to shape the future of fintech and healthtech? Energized by challenges and inspired by bold goals? Ready to elevate your career alongside driven and talented colleagues? If that sounds like you, explore a career at Alegeus today. Opportunity Happens Here.

Expert QA Engineer – Data Platform

Location

Bangalore, India

About Alegeus

Alegeus is the market leader in consumer-directed healthcare (CDH) solutions, powering millions of consumer benefit accounts including FSAs, HSAs, HRAs, dependent care and wellness programs through a modern SaaS and payments platform.

We are investing aggressively in modernization, API-first integration, real-time data access, and AI-enabled automation to redefine how consumers save and spend on healthcare. At this inflection point, we are transforming our platform, elevating engineering rigor, and building a next-generation product and engineering organization.

Role summary

We are looking for an Expert QA Engineer to ensure the quality, reliability, and correctness of our next-generation Data Platform and Data-Driven APIs.

This role focuses on validating large-scale distributed data systems (Apache Spark), data pipelines, and data-serving platforms (APIs/microservices). You will operate at the intersection of data engineering and quality engineering, building automated frameworks and strategies that ensure data is accurate, complete, and trustworthy at scale.

This is not traditional QA. You will validate systems where failures are often silent—requiring strong analytical thinking, deep data validation skills, and a mindset that treats data correctness as a first-class concern.

Key responsibilities

Data Platform Quality Engineering

• Design and develop scalable data pipelines using Apache Spark (batch and streaming)

• Build and maintain data platform layers: ingestion, transformation, and serving

• Optimize Spark jobs for performance, cost, and reliability (partitioning, skew handling, memory tuning)

• Implement data quality, observability, and lineage frameworks

• Contribute to data architecture decisions (Lakehouse, data mesh, storage formats, partition strategies)

• Define and enforce data contracts and schema evolution practices

Test Strategy & Automation

• Design and implement end-to-end test strategies for ETL/ELT pipelines

• Build and maintain automated data validation frameworks (SQL-based, programmatic, or custom tools)

• Develop reusable utilities for data comparison, reconciliation, and anomaly detection

• Integrate data validation into CI/CD pipelines for continuous quality checks

API & Data Serving Validation

• Validate data exposed via APIs and microservices (Java/.NET/Node) for correctness and contract adherence

• Perform integration testing across data pipelines and consuming services

• Ensure low-latency and high-availability data serving layers meet functional and non-functional requirements

Data Quality, Observability & Monitoring

• Define and enforce data quality rules (accuracy, completeness, timeliness, uniqueness)

• Implement and leverage data observability tools to detect anomalies, drift, and failures

• Validate pipeline reliability, including retry mechanisms and failure recovery

• Collaborate in defining SLAs/SLOs for data availability and freshness

Performance & Edge Case Validation

• Test pipelines and systems for performance at scale, including large data volumes and concurrency

• Validate handling of edge cases such as:

  • Data skew and partition imbalance

  • Late-arriving or out-of-order data

  • Duplicate records and idempotency

  • Schema evolution and backward compatibility

Collaboration & Product Mindset

• Work closely with data engineers, backend engineers, product managers, and analytics teams

• Understand business meaning of data, not just structure

• Translate business rules into testable validation logic

• Participate in design discussions to prevent defects early (shift-left approach)

Required qualifications (Hard requirements)

• 8+ years of experience in QA / Quality Engineering, with strong focus on data platforms or data systems

• Hands-on experience testing data pipelines (ETL/ELT) and distributed systems

• Strong SQL skills and experience with large-scale data validation

• Experience with Apache Spark or similar distributed data processing frameworks

• Programming skills in Python / Java / Scala for test automation

• Experience validating REST APIs and microservices

• Deep understanding of:

  • Data validation techniques (reconciliation, aggregation checks, anomaly detection)

  • Data storage formats (Parquet, ORC, Avro)

  • Data modeling and schema evolution

• Experience with cloud platforms (Azure/AWS/GCP)

• Familiarity with CI/CD pipelines and test automation frameworks

Preferred qualifications

• Experience with Spark Structured Streaming and real-time data validation

• Exposure to Lakehouse architecture (Delta Lake, Iceberg, Hudi)

• Experience with data observability tools (e.g., Monte Carlo, Great Expectations, Deequ, etc.)

• Understanding of event-driven systems (Kafka, Event Hubs)

• Experience with performance and load testing for data systems

• Familiarity with Kubernetes and containerized environments

What success looks like

A successful QA engineer in this role will:

• Ensure high data correctness and reliability across pipelines and APIs

• Build automated validation systems that scale with data growth

• Detect and prevent silent data failures and inconsistencies

• Collaborate effectively to shift quality upstream into design and development

• Improve confidence in data as a trusted product asset

Why join us

You will work on mission-critical data systems where correctness directly impacts financial and healthcare outcomes. This role offers the opportunity to define how data quality is engineered at scale, not just tested.

You will collaborate with strong engineering teams, work on modern data architecture, and help build a platform where data is treated as a product-not a byproduct.

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