Cloud Data Engineer

Role Overview:

Responsible for developing, deploying, and optimizing scalable data pipelines, data models, and analytics solutions to support enterprise-wide business insights and decision-making. This role requires expertise in cloud data engineering, data warehousing, advanced SQL, and Python/PySpark transformations, with the ability to translate data into actionable business recommendations. The ideal candidate brings deep technical experience, strong analytical skills, and cross-functional collaboration abilities, preferably within the pharmaceutical or healthcare domain.

Key Responsibilities:

• Develop and maintain scalable ETL/ELT pipelines using Python, PySpark, and SQL for structured and unstructured data ingestion.
• Build robust data infrastructure on cloud platforms such as Azure Fabric, Synapse, Databricks, and AWS.
• Implement end-to-end automation for data ingestion, streaming, scheduling, and monitoring across Azure Functions, Data Factory, ADLS Gen2, and Power BI.
• Optimize large-scale big data architectures for performance and scalability.
• Design and maintain multidimensional models (Star/Snowflake Schema) including fact and dimension tables, views, and stored procedures.
• Manage complex datasets ensuring alignment with functional and non-functional requirements.
• Learn foundational SAP S4/HANA and BW4 data models to support financial analytics.
• Utilize GitHub and Azure DevOps for CI/CD, version control, and automated deployments.
• Manage artifact deployment across environments with risk assessments and impact analysis.
• Implement process improvements and workflow automation.
• Partner with business stakeholders to gather requirements and translate them into analytical solutions.
• Develop analytics and reporting solutions supporting Global Finance transformation strategy.
• Develop dashboards and BI tools using Power BI/Tableau, translating outcomes into actionable insights with KPIs.
• Troubleshoot data-related issues and perform root cause analysis.

Required Skills:

• 5-8 years of hands-on experience in data engineering and cloud data warehousing (Azure Synapse, Fabric, Databricks, Redshift, or Snowflake).
• Expert SQL proficiency across relational databases and cloud data warehouses, plus strong Python and PySpark skills for data transformation and pipeline development.
• Data modelling expertise including dimensional modelling (facts, dims), normalization/de-normalization, views, and stored procedures for automation.
• End-to-end ownership of project delivery with proven ability to translate business problems into scalable analytical solutions.
• Big data architecture experience building and optimizing large-scale pipelines across structured and unstructured datasets.
• Visualization expertise with Power BI and/or Tableau to present complex findings clearly.
• Strong documentation and communication skills to support operational standards and collaborate with cross-functional teams.
• Experience with root cause analysis, continuous improvement, and Agile delivery methodologies.

Qualifications:

• Bachelor's or Master's degree in Technology, Computer Science, Engineering, or related field.

Preferred Skills:

• Experience with SQL and NoSQL databases (AWS Redshift, Postgres, Databricks etc.).
• Pharmaceutical or healthcare dataset experience.
• Cloud expertise in AWS/Azure; Microsoft Fabric experience is a strong advantage.
• CI/CD experience using GitHub or Azure DevOps.
• Proficiency in Python/PySpark, R, Scala, or additional scripting languages.
• AI & Gen AI - Products & Tools.