Data Scrum Master/Quality Lead

Zweizag; Chennai District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Zweizag in Chennai seeks a Parking Domain Data Scrum Master/Quality Lead to guide a cross-functional team through the end-to-end data lifecycle powering ADAS parking features. You will combine servant leadership with test-management expertise to establish robust validation for data pipelines and products, champion data integrity, and drive agile ceremonies and release readiness across the data assets.

This role requires collaboration with product, engineering, and safety teams to ensure

Qualifications

• Proven experience as Scrum Master or Agile Coach in a fast-paced technology environment.
• Strong background in Test Management, Quality Assurance, or SDET leadership.
• Deep understanding of Agile methodologies (Scrum, Kanban) and when to pragmatically apply them.
• Excellent facilitation, conflict resolution, and communication skills.
• Proficiency with Agile lifecycle and test management tools; Domain Expertise (ADAS & Automotive).
• Experience supporting data engineering, machine learning operations (MLOps), or data pipeline teams.
• Understanding data lifecycles and data integrity challenges in ADAS contexts.

Responsibilities

• Agile Coaching & Delivery Facilitation: lead ceremonies and adapt Scrum/Kanban to data and software tasks.
• Partnership with the Data Product Owner to refine backlog and align with ADAS feature development.
• Data Quality & Validation Governance: ensure DoD includes data validation gates and dataset quality metrics.
• Test Strategy & Pipeline Methodology: drive shift-left testing and end-to-end data validation.
• Release Readiness & Traceability: coordinate UAT and ensure traceability from requirements to data release.

Skills

Scrum Master

Agile Coach

Agile Delivery Lead

Test Management

Quality Assurance

SDET Leadership

Facilitation

Conflict Resolution

Communication

Agile Lifecycle

ADAS Automotive

Data Engineering Support

MLOps

Data Pipelines

Sensor Data

Job description

Parking Domain Data Scrum Master/Quality Lead

Experience: 5 to 8 Years
Location: Chennai

Role Summary

The Scrum Master & Quality Lead will partner with our Data Product Owner (DPO)and guide a cross-functional team of software developers, data engineers, andoperations specialists. This team owns the end-to-end data lifecyclefrom vehiclecollection and ingestion to processing, annotation, and releasepowering ourAdvanced Driver Assistance Systems (ADAS) Parking features (Perception, ParkingFunctions, and Crowd-Sourced Maps).
In this dual-purpose role, you will act as a servant-leader to optimize team workflowsand Agile practices, while leveraging your strong test management background toestablish robust validation methodologies for our data pipelines and final dataproducts. Because high-quality data is safety-critical for ADAS, you will champion aculture where data integrity is treated with the same rigor as software code.

Key Responsibilities

• 1. Agile Coaching & Delivery Facilitation
  • Facilitate standard Agile ceremonies, including Daily Stand-ups, Sprint Planning, Sprint Reviews, and Retrospectives.
  • Guide the team in Agile best practices, adapting frameworks (Scrum/Kanban) to suit a unique blend of software engineering and operational data tasks.
  • Track and communicate key delivery metrics (e.g., sprint burndown, velocity, and cycle time for data processing).
  • Actively identify and remove technical or operational impediments thathinder team progress.
• 2. Partnership with the Data Product Owner (DPO)
  • Collaborate closely with the DPO to ensure the data product backlog is refined, prioritized, and aligned with downstream ADAS feature development.
  • Assist in breaking down complex data pipeline requirements into clear, manageable epics and user stories.
  • Manage cross-team dependencies, safeguarding the team from scopecreep and external distractions.
• 3. Data Quality & Validation Governance
  • Champion the Definition of Done (DoD) by ensuring it explicitly includes data validation gates, schema checks, and data contract adherence before any story is closed.
  • Establish and monitor validation workflows for the annotation process, ensuring metrics like Inter-Annotator Agreement (IAA) and groundtruth accuracy meet strict ADAS thresholds.
  • Define statistical sampling and validation frameworks for incoming, often noisy, crowd-sourced parking map data.
• 4. Test Strategy & Pipeline Methodology
  • Drive a \"Shift-Left\" data testing culture by facilitating the integration of automated data testing (e.g., anomaly detection, automated schema checks) early in the ingestion and processing pipelines.
  • Coordinate with data engineers to apply robust testing methodologies (Unit, Integration, and End-to-End) to both the pipeline code and the data payloads themselves.
  • Lead defect triage sessions specifically focused on data anomalies, differentiating between software pipeline defects and fundamental data quality issues (e.g., sensor calibration).
• 5. Release Readiness & Traceability
  • Act as the testing liaison for User Acceptance Testing (UAT) with downstream ADAS feature developers, ensuring released datasets physically function in their simulation and training environments.
  • Ensure full traceability from initial driving campaign requirements through to the final validated data release, supporting automotive safety and compliance audits.

Qualifications & Skills

• Proven experience as a Scrum Master, Agile Coach, or Agile Delivery Lead in a fast-paced technology environment.
• Deep background in Test Management, Quality Assurance, or SDET leadership, with a strong grasp of modern testing frameworks and methodologies.
• Deep understanding of Agile methodologies (Scrum, Kanban) and when topragmatically apply them.
• Excellent facilitation, conflict resolution, and communication skills.
• Proficiency with Agile lifecycle and test management tools Domain Expertise (ADAS & Automotive)
• Previous experience supporting data engineering, machine learning operations (MLOps), or data pipeline teams.
• Understanding of the unique challenges in data lifecycles compared to traditional software development (e.g., managing physical data collection logistics or human-in-the-loop annotation).
• Familiarity with the automotive industry, ADAS, autonomous driving environments, or sensor data (Cameras, Radar, LiDAR, UltrasONIC