Senior Data Engineer

About the role

Design, build, and optimize automated data pipelines, ETL/ELT processes, and data models to ingest, process, and store large volumes of data within cloud-based platforms. Partner with business, analytics, and product teams to translate data requirements into effective technical solutions that support strategic initiatives.

Qualifications

• 8 years or above of total Data Engineering experience, with strong exposure to large-scale data pipelines, ETL/ELT development, and enterprise or cloud-based data platforms
• Proven experience designing, building, and optimizing scalable data solutions in modern data environments
• Python – At least 4 out of 5 proficiency level, with strong hands-on experience in data transformation, automation, and pipeline development
• SQL – At least 3 out of 5 proficiency level, with demonstrated capability in complex queries, data modeling, and performance tuning
• Experience working with modern data cloud platforms, such as Databricks and/or Snowflake
• Experience with cloud services, preferably Microsoft Azure (e.g., Azure Data Factory, Azure Synapse, Azure Storage, etc.)
• Strong verbal and written communication skills
• Demonstrated leadership and technical influence
• Strong analytical, critical thinking, and problem-solving abilities
• Stakeholder and cross-functional collaboration skills

Key responsibilities

• Design, build, and optimize automated data pipelines, ETL/ELT processes, and data models to ingest, process, and store large volumes of data within cloud-based platforms
• Support large-scale data migration initiatives, ensuring data accuracy, performance efficiency, and minimal business disruption
• Develop and maintain ETL/ELT workflows to ingest, transform, and load data from multiple internal and external sources with a focus on scalability and reliability
• Partner with business, analytics, and product teams to translate data requirements into effective technical solutions that support strategic initiatives
• Design and deliver data marts and customized data extractions aligned with business and reporting needs
• Ensure compliance with enterprise data governance, security, and regulatory standards
• Monitor data pipeline health and performance, troubleshoot data incidents, and implement preventive and corrective measures
• Document data workflows, schemas, technical specifications, and operational runbooks to support operational stability and knowledge transfer
• Collaborate closely with product owners, data architects, and data scientists to maintain a reliable and efficient data infrastructure
• Drive continuous improvement of data engineering practices, tools, and automation frameworks