Data Quality Engineer

Quality Engineer Data

Key Responsibilities:

• Design and implement robust QA strategies for

  data pipelines

  ,

  ML models

  , and

  agentic workflows

  .
• Test and validate

  data ingestion and streaming systems

  (e.g.,

  StreamSets

  ,

  Kafka

  ) for accuracy, completeness, and resilience.
• Ensure

  data integrity and schema validation

  within

  MongoDB

  and other data stores.
• Collaborate with

  Data Engineers

  to proactively identify and resolve data quality issues.
• Partner with

  Data Scientists

  to validate ML/DL/LLM model performance, fairness, and robustness.
• Automate testing processes using frameworks such as

  Pytest

  ,

  Great Expectations

  , and

  Deepchecks

  .
• Monitor

  production pipelines

  for anomalies,

  data drift

  , and

  model degradation

  .
• Participate in

  code reviews

  , QA audits, and maintain

  comprehensive documentation

  of test plans and results.
• Continuously evaluate and improve QA processes based on

  industry best practices

  and emerging trends.

Required Technical Skills:

• 510 years of QA experience with a focus on

  data validation

  and

  ML model testing

  .
• Strong command of

  SQL

  for complex data queries and integrity checks.
• Practical experience with

  StreamSets

  ,

  Kafka

  , and

  MongoDB

  .
• Proficient in

  Python scripting

  for automation and testing.
• Familiarity with

  ML testing metrics

  , model validation techniques, and

  bias detection

  .
• Exposure to

  cloud platforms

  such as

  Azure

  ,

  AWS

  , or

  GCP

  .
• Working knowledge of

  QA tools

  like

  Pytest

  ,

  Great Expectations

  , and

  Deepchecks

  .
• Understanding of

  Node.js

  and

  React-based applications

  is an added advantage.

Additional Qualifications:

• Excellent

  communication

  ,

  documentation

  , and

  cross-functional collaboration

  skills.
• Strong analytical mindset and high attention to detail.
  
• Ability to work with cross-disciplinary teams including Engineering, Data Science, and Product.
  
• Passion for continuous learning and adoption of new QA tools and methodologies.
  

• Domain knowledge

  in

  facility management

  ,

  IoT

  , or

  building automation systems

  is a strong plus.