2 hours ago
Hyderābād, IndiaStaff+
Responsibilities
- Define and drive end-to-end quality strategy for data platforms and engineering systems.
- Architect scalable automation frameworks for batch, streaming, API, and UI validation.
- Establish data quality governance, validation standards, and KPIs.
- Design automated reconciliation, integrity, and observability validation.
- Lead test strategy for large-scale data migrations and cloud transformation initiatives.
- Integrate quality gates into CI/CD and DevOps pipelines.
- Drive performance, reliability, and security validation frameworks.
- Mentor QA engineers and provide technical leadership.
- Collaborate with architects on data mesh and lakehouse validation strategy.
- Evaluate and introduce AI-driven testing and automation accelerators.
Requirements
- Bachelor’s or master’s degree in Computer Science or a related field.
- 8–12+ years of experience in Quality Engineering with a strong data and platform focus.
- At least 2 years in a technical leadership or quality ownership role.
- Advanced Python and expert-level SQL skills.
- Strong understanding of data architecture, ETL optimization, and pipeline validation.
- Experience leading quality initiatives in cloud-based ecosystems.
- Experience with Snowflake, Redshift, BigQuery, Spark, and Kafka streaming validation.
- Experience with AWS, Azure, or GCP and with Airflow, dbt, or Glue for data orchestration validation.
- Experience designing backend and data automation frameworks and validating performance and scalability.
- Exposure to data governance, compliance, and security validation.
- Experience with test architecture, quality metrics and KPI governance, mentoring, strategic innovation, and executive-level stakeholder communication.
Tech Stack
Amazon RedshiftApache AirflowApache KafkaApache SparkAWSAzuredbtGoogle BigQueryGoogle Cloud PlatformPythonSnowflakeSQL
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