Bigdata AWS engineer- onsite

Job Summary

Responsibilities

Qualifications

Skills

Benefits

Pay

Schedule

Lead end to end s planning for data driven and automation heavy projects that rely on prompt engineering AWS data pipelines Python solutions and big data validation to ensure consistently reliable releases.

Design detailed s strategies that cover functional flows data integrity checks regression suites and non functional aspects so that all critical quality risks are identified and mitigated early.

Capture and coordinate s case design using systematic techniques and reusable patterns to maximize coverage of data transformation rules automation scenarios and device oriented business workflows.

Develop robust Python based automation scripts and reusable libraries that validate large data sets orchestrate AWS pipeline jobs and reduce manual sing effort across repeated cycles.

Configure execute and monitor AWS data pipeline ss by validating data ingestion transformation scheduling and downstream consumption so that data quality and timeliness remain predictable.

Create and maintain s data strategies that include synthetic data design masking rules and data refresh routines to keep s environments stable and representative of real world usage.

Execute day shift s cycles in a hybrid work model while collaborating closely with distributed engineering data and product teams to keep progress transparent and aligned to milestones.

Track analyze and report defects with clear steps logs and data samples to support fast triage accurate root cause analysis and timely resolution by development teams.

Review prompt engineering use cases and outputs from AI enabled components to verify accuracy safety and consistency with expected business behavior and compliance guidelines.

Collaborate with stakeholders from device engineering and device operations to understand domain scenarios translate them into s cases and validate that solutions support end user needs.

Prepare regular s progress summaries risk assessments and quality metrics that inform decision making and help the organization release reliable solutions that support customer trust.

Continuously improve s frameworks automation coverage and data validation practices by analyzing past issues and adopting modern tools that increase efficiency without compromising quality.

Skills

• Strong experience designing and executing ss for AWS data pipelines including data flow validation orchestration checks and monitoring of data quality indicators.
• Demonstrate advanced proficiency in Python automation for building modular s frameworks integration suites and data validation utilities that scale to large volumes.
• Apply practical knowledge of prompt engineering concepts to evaluate prompts expected responses and guardrails for AI driven features from a sing perspective.
• Bring background in device engineering or device focused domains as a beneficial asset for understanding hardware software interactions and real world usage patterns.
• Show capability to work effectively in a hybrid environment by managing time communication and collaboration tools to support on site and remote team members.
• Exhibit experience of six to ten years in software quality engineering or s roles with increasing responsibility for complex data and automation initiatives.
• Communicate clearly with technical and non technical partners through concise documentation s reports and walkthroughs that enable shared understanding of risks and outcomes.

Benefits

• Hybrid work model allowing flexibility between on-site and remote work.
• Opportunities for professional growth and continuous learning through adoption of modern tools and practices.
• Support for maintaining high-quality standards through rigorous testing and validation processes.
• Collaborative environment fostering innovation and problem-solving among diverse teams.

Pay

Competitive compensation package commensurate with experience.

Schedule

Perform day shift s cycles in a hybrid work model.