Key Responsibilities

• Data Engineering & ETL/ELTDevelop, maintain, and monitor ETL/ELT pipelines for structured and unstructured data from multiple sources.
• Support data ingestion, transformation, integration, and processing activities.
• Troubleshoot pipeline and data-processing issues to maintain reliable data flows.

• Data Quality & ReliabilityPerform data cleansing, validation, reconciliation, and quality checks.
• Identify data inconsistencies and work with relevant teams to resolve data-quality issues.
• Ensure datasets are accurate, consistent, and reliable for downstream analytics and reporting.

• Data Modeling & TransformationDesign and maintain reusable datasets, data models, and semantic layers.
• Apply data warehousing concepts, including dimensional modeling and star schemas.
• Apply appropriate software design patterns and SOLID principles when developing reusable data components.

• Analytics & VisualizationSupport the development of interactive dashboards and automated reports using Tableau or similar BI tools.
• Prepare analytics-ready datasets to support business KPIs and management reporting.

• AI & AutomationUtilize modern AI-assisted tools to accelerate data querying, scripting, development, and automation.
• Validate AI-generated outputs and ensure compliance with organizational data governance and security standards.
• Identify opportunities to automate repetitive data-engineering activities.

• Stakeholder CollaborationWork closely with data scientists, software engineers, analysts, and business stakeholders.
• Translate business requirements into structured, scalable, and practical data solutions.
• Communicate technical concepts effectively to both technical and non-technical stakeholders.

• Documentation & GovernanceMaintain technical documentation, data dictionaries, data mappings, and operational procedures.
• Follow established data governance, security, and quality standards.

Key Responsibilities

• Data Engineering & ETL/ELTDevelop, maintain, and monitor ETL/ELT pipelines for structured and unstructured data from multiple sources.
• Support data ingestion, transformation, integration, and processing activities.
• Troubleshoot pipeline and data-processing issues to maintain reliable data flows.

• Data Quality & ReliabilityPerform data cleansing, validation, reconciliation, and quality checks.
• Identify data inconsistencies and work with relevant teams to resolve data-quality issues.
• Ensure datasets are accurate, consistent, and reliable for downstream analytics and reporting.

• Data Modeling & TransformationDesign and maintain reusable datasets, data models, and semantic layers.
• Apply data warehousing concepts, including dimensional modeling and star schemas.
• Apply appropriate software design patterns and SOLID principles when developing reusable data components.

• Analytics & VisualizationSupport the development of interactive dashboards and automated reports using Tableau or similar BI tools.
• Prepare analytics-ready datasets to support business KPIs and management reporting.

• AI & AutomationUtilize modern AI-assisted tools to accelerate data querying, scripting, development, and automation.
• Validate AI-generated outputs and ensure compliance with organizational data governance and security standards.
• Identify opportunities to automate repetitive data-engineering activities.

• Stakeholder CollaborationWork closely with data scientists, software engineers, analysts, and business stakeholders.
• Translate business requirements into structured, scalable, and practical data solutions.
• Communicate technical concepts effectively to both technical and non-technical stakeholders.

• Documentation & GovernanceMaintain technical documentation, data dictionaries, data mappings, and operational procedures.
• Follow established data governance, security, and quality standards.