TECHNICAL LEAD - LEAD

JD :

Job Location : Pune & Bangalore

Experience Required : 8-11 yrs.

Required Skills & Qualifications

Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.

Strong hands-on experience in testing batch and streaming data pipelines.

Expertise with BigQuery or equivalent (Redshift, Synapse, etc.).

Experience with Snowflake, Looker, or similar analytics systems on AWS or Azure.

Advanced SQL skills joins, window functions, CTEs, query optimization, and complex validation queries.

Solid knowledge of Python for data validation and test automation.

Experience working with Airflow DAGs and data orchestration frameworks.

Proven experience in data modeling, synthetic data generation, and defining data validation rulesets.

AI & Automation-Specific Expertise (Mandatory / Optional / Nice to Have)

Skill AreaRequirement LevelDetailsPrompt EngineeringMandatoryStrong ability to design prompts for data validation, test generation, and automation assistance.Codex / GitHub Copilot / CursorMandatorySkilled in AI-assisted scripting with accurate context, guardrails, and coding rules.MCP Implementation & UsageMandatoryHands-on experience configuring and leveraging MCP for intelligent test data and metadata management.OpenAI APIsOptionalExperience in integrating APIs for test data synthesis, log analysis, or anomaly detection.LLM ConfigurationOptionalExposure to fine-tuning or parameter-level adjustments to enhance model-assisted validation.LLM FundamentalsNice to HaveUnderstanding of prompt context length, tokenization, and reasoning limits in LLMs.AI Agents for Data ValidationNice to HaveExperience in using or developing AI Agents for pipeline health checks, dynamic test selection, or automated data integrity scoring.

Communication & Soft Skills

Excellent communication and cross-functional collaboration skills, especially with Data Engineering, ML, and Analytics teams.

Strong ability to translate data validation results into actionable insights for technical and business stakeholders.

Demonstrated problem-solving and analytical thinking in complex data environments.

Mentorship experience and the ability to foster a culture of quality-first data engineering.

Self-driven, adaptable, and comfortable with fast-paced, evolving AI and data ecosystems.

What We Offer

Opportunity to build AI-augmented testing frameworks that ensure data reliability at scale.

Exposure to cutting-edge cloud data ecosystems (GCP, AWS, Azure) and next-gen test automation platforms.

Collaborative, data-driven culture focused on innovation and continuous learning.

Competitive compensation, benefits, and opportunities to shape the future of AI-driven data validation.