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.
Provide technical guidance and mentorship to junior engineers through code reviews, best-practice sharing, and troubleshooting support.
Manage and deliver multiple data engineering initiatives concurrently while meeting quality, scope, and timeline expectations.
Must Have
8 years 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.).
Platform and Cloud Exposure - Currently working with or has recent hands-on experience in enterprise-grade cloud data ecosystems. With a strong understanding of cloud-native data architecture and best practices.
Strong verbal and written communication skills
Demonstrated leadership and technical influence
Strong analytical, critical thinking, and problem-solving abilities
Process orientation with the ability to enforce standards and best practices
Strong organizational, multitasking, and time-management skills
Stakeholder and cross-functional collaboration skills