Data Engineer

Job Description: 

● 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. 

Requirements: 

● Experience 

○ At least 8 years 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. 

● Knowledge 

○ 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.