MODULE LEAD - LEAD

Job Title:

Experience Level:

About the Role

Senior Data Quality & Validation Engineer

cloud-based ecosystems (AWS, Azure, GCP)

Key Responsibilities

• Design, implement, and execute

  comprehensive test strategies

  for both

  batch and streaming data pipelines

  .
• Develop and maintain

  data validation and reconciliation frameworks

  to ensure accuracy and consistency across data layers.
• Perform complex

  SQL-based data validation

  including joins, window functions, CTEs, and performance optimization.
• Collaborate with data engineering teams to validate

  data models

  , perform

  synthetic data generation

  , and define

  data quality rulesets

  .
• Integrate and automate data quality checks using

  Python

  ,

  Airflow DAGs

  , and orchestration frameworks.
• Work with

  BigQuery

  ,

  Redshift

  ,

  Synapse

  , or

  Snowflake

  for analytical data validation and performance tuning.
• Utilize

  Looker

  ,

  Power BI

  , or equivalent BI tools for data consistency verification and visualization-based testing.
• Apply

  Prompt Engineering techniques

  to enhance test automation and data validation workflows.
• Implement and leverage

  MCP (Model Context Protocol)

  for AI-assisted data validation and integration with LLM-driven systems.
• Collaborate on

  AI-based data validation and testing tools

  leveraging

  Codex, GitHub Copilot, or Cursor

  .
• Optionally, experiment with

  OpenAI APIs

  and

  LLM configuration

  to improve automation in data quality processes.
• Support exploration and orchestration of

  AI agents

  for data pipeline monitoring and validation workflows.

Required Qualifications

• Bachelor’s or Master’s degree

  in Computer Science, Data Engineering, or a related technical field.

• 5–8 years of hands-on experience

  testing and validating

  data pipelines

  (batch and streaming).
• Deep expertise with

  BigQuery

  ,

  Redshift

  ,

  Synapse

  , or equivalent data warehousing systems.
• Experience with

  Snowflake

  and

  Looker

  or similar analytics/BI platforms on

  AWS

  or

  Azure

  .

• Advanced SQL proficiency

  — joins, CTEs, window functions, query optimization, and large-scale data validation.
• Strong command of

  Python

  for

  data validation

  ,

  automation scripting

  , and

  framework integration

  .
• Working knowledge of

  Airflow

  or other

  data orchestration tools

  .
• Proven experience in

  data modeling

  ,

  synthetic data creation

  , and defining

  data validation rule sets

  .

• Mandatory:

  Prompt Engineering, Codex/GitHub Copilot/Cursor usage, and MCP implementation & usage.

Preferred / Nice-to-Have Skills

• Familiarity with

  OpenAI APIs

  and

  LLM configuration

  .
• Understanding of

  LLM fundamentals

  and

  AI Agent orchestration

  .
• Experience integrating

  AI-driven automation

  within data engineering pipelines.

Soft Skills

• Strong analytical and problem-solving abilities.
• Excellent communication and collaboration skills across technical and non-technical teams.
• Self-driven, detail-oriented, and comfortable working in a fast-paced, innovation-driven environment.

Why Join Us

• Work at the intersection of

  Data Engineering and Generative AI

  .
• Contribute to next-generation

  AI-integrated data validation frameworks

  .
• Collaborate with a team passionate about innovation, automation, and data excellence.