Big Data Engineer (H)

Design, develop, maintain, and optimize data pipelines and data processing workflows.

• Develop and maintain solutions using Python, PySpark, Spark SQL, SQL, Hadoop/Hive, MongoDB and Apache Spark.
• Work with batch and real-time/streaming data processing.
• Support data modeling, data transformation, and data integration activities.
• Analyze large datasets to identify trends, anomalies, data quality issues, and opportunities for improvement.
• Address and resolve defects and performance issues in data pipelines.
• Support architectural changes and modernization initiatives.
• Identify opportunities for automation, performance optimization, and operational efficiency.

Platform Production Operations

• Perform daily monitoring of METIS scheduled jobs, microservices, applications, data pipelines, and Power BI report refreshes.
• Monitor and manage workflows and schedules using Apache Airflow.
• Perform configurable changes, scheduling, restart/reprocessing activities, and operational troubleshooting.
• Perform manual/controlled data refreshes for various enterprise data sources.
• Troubleshoot failed jobs, data pipeline failures, application issues, and data quality problems.
• Perform root-cause analysis and implement sustainable solutions.
• Support production releases, deployments, and operational activities.
• Ensure platform availability, stability, and data freshness.

Incident, Problem Change Management

• Handle Incident, Problem and Change tickets in accordance with ITIL processes.
• Perform root-cause analysis for recurring incidents and data issues.
• Coordinate with relevant development, infrastructure, application, and source-system teams for issue resolution.
• Maintain operational documentation and knowledge articles.
• Participate in production support and provide timely communication to stakeholders.

Data Quality Testing

• Perform data quality checks during development and production.
• Identify, analyze, and resolve data inconsistencies and data quality issues.
• Conduct unit, integration, regression, and QA testing.
• Validate data pipelines, transformations, reports, and downstream data consumption.
• Establish and improve data quality and monitoring mechanisms.

Stakeholder Requirement Management

• Act as a technical interface between business stakeholders, data engineering teams, application teams, architects, infrastructure teams, and data consumers.
• Understand business needs and translate them into clear technical/data requirements.
• Conduct requirement discussions, clarification sessions, and impact assessments.
• Identify new business use cases that can be enabled using existing METIS data.
• Prepare and maintain requirements, technical specifications, process documentation, and solution proposals.
• Communicate technical topics and issues clearly to both technical and non-technical stakeholders.
• Coordinate cross-functional activities and ensure timely delivery of agreed requirements.
• Proactively identify risks, dependencies, and potential improvements.

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