Job Summary

Responsibilities

• Design, develop, and maintain scalable data pipelines to ingest, process, transform, and curate data from multiple enterprise and eCommerce data sources.
• Build and support ELT/ETL workflows using AWS, Databricks, dbt, Spark, Python, and SQL.
• Develop and maintain reliable, reusable, and high-quality data products that support reporting, analytics, and business intelligence initiatives.
• Implement data transformation and modelling solutions aligned with Medallion Architecture principles.
• Collaborate with business stakeholders, analysts, and product teams to understand requirements and translate them into scalable technical solutions.
• Perform data profiling, validation, reconciliation, and quality monitoring to ensure trusted business data.
• Support analytical and reporting solutions by designing and preparing datasets for Power BI and other reporting tools.
• Analyze data trends and identify opportunities to improve operational performance, data quality, and business outcomes.
• Contribute to the continuous improvement of data engineering standards, platform capabilities, data governance, and best practices.
• Troubleshoot pipeline failures, performance bottlenecks, and data quality issues through root cause analysis and corrective actions.
• Partner with engineering and business teams to deliver scalable solutions aligned to enterprise architecture, security, and governance standards.
• Continuously evaluate and adopt modern engineering practices and AI-enabled productivity capabilities to improve delivery efficiency and solution quality

Key Responsibilities

Responsibilities

• Design, develop, and maintain scalable data pipelines to ingest, process, transform, and curate data from multiple enterprise and eCommerce data sources.
• Build and support ELT/ETL workflows using AWS, Databricks, dbt, Spark, Python, and SQL.
• Develop and maintain reliable, reusable, and high-quality data products that support reporting, analytics, and business intelligence initiatives.
• Implement data transformation and modelling solutions aligned with Medallion Architecture principles.
• Collaborate with business stakeholders, analysts, and product teams to understand requirements and translate them into scalable technical solutions.
• Perform data profiling, validation, reconciliation, and quality monitoring to ensure trusted business data.
• Support analytical and reporting solutions by designing and preparing datasets for Power BI and other reporting tools.
• Analyze data trends and identify opportunities to improve operational performance, data quality, and business outcomes.
• Contribute to the continuous improvement of data engineering standards, platform capabilities, data governance, and best practices.
• Troubleshoot pipeline failures, performance bottlenecks, and data quality issues through root cause analysis and corrective actions.
• Partner with engineering and business teams to deliver scalable solutions aligned to enterprise architecture, security, and governance standards.
• Continuously evaluate and adopt modern engineering practices and AI-enabled productivity capabilities to improve delivery efficiency and solution quality

Skill Requirements

Required  Skills

• 4+ years of experience as a Data Engineer
• Strong proficiency in SQL for data analysis, transformation, optimization, and data modelling.
• Hands-on experience developing scalable data pipelines using Python and Apache Spark.
• Experience working with Databricks for large-scale data processing and analytics engineering.
• Experience using dbt to develop, test, document, and deploy data transformation frameworks.
• Strong understanding of data warehousing concepts, dimensional modelling, Medallion Architecture, and modern data platform design.
• Experience building trusted datasets and semantic models that support business intelligence and reporting solutions, including Power BI.
• Understanding of data quality, metadata management, lineage, governance, and operational monitoring.
• Familiarity with source control, CI/CD practices, testing frameworks, and automated deployment processes.
• Hands-on experience leveraging AI-assisted development and productivity tools (such as Microsoft Copilot, Copilot CLI, Databricks Genie, and similar AI-powered assistants) to accelerate development of data pipelines, data models, analytical datasets, and reporting solutions while improving code quality, documentation, troubleshooting, and delivery efficiency.
• Understanding of responsible AI practices, including validation of AI-generated outputs, dataprivacy, security, and governance considerations.
• Strong analytical and problem-solving capabilities, with the ability to translate business requirements into scalable technical solutions.
• Effective collaboration and communication skills, with the ability to work across business, analytics, and engineering teams in a global environment.
• Demonstrated ownership mindset with a strong focus on quality, continuous improvement, and delivering business value.

Other Requirements

Desired Skills

• Experience working with eCommerce, digital commerce, customer journey, product catalogue, pricing, transaction, order management, or digital channel data.
• Experience designing cloud-native data products and analytics solutions at enterprise scale.
• Exposure to Scala for distributed data processing workloads.
• Experience supporting advanced analytics, machine learning, and AI-driven business solutions.
• Familiarity with data observability, data quality frameworks, and data governance platforms.
• Experience working within Agile product teams and modern software development methodologies.
• Knowledge of real-time, streaming, or event-driven data architectures.

Experience working with AWS cloud services and cloud-native data engineering architectures