Resumen del puesto por JobGrid
L2 Data Engineer - Remote at DeepSource Technologies: Remoto, Bangladés; Jornada completa. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Remoto, Bangladés
- Role classification: Jornada completa
- Source freshness: checked by JobGrid on 2026-05-31.
- Application path: candidates continue to the employer application page with non-personal referral tags.
We are looking for a highly skilled and experienced L2 Data Engineer to join our growing Data & Analytics team. In this role, you will lead the design, development, optimization, and maintenance of scalable enterprise data platforms and cloud-native data solutions. You will work closely with architects, analysts, and business stakeholders to build high-performance data pipelines and modern lakehouse solutions that support advanced analytics, reporting, and data-driven decision-making.
This opportunity is ideal for a senior data professional with strong hands-on expertise in Databricks and the Microsoft Azure ecosystem, who is passionate about building reliable, scalable, and optimized data platforms in enterprise environments.
KEY RESPONSIBILITIES
• Design, develop, and optimize enterprise-scale data pipelines and ETL/ELT workflows using Azure and Databricks technologies.
• Architect and implement scalable data ingestion, transformation, and orchestration processes using Azure Data Factory, Databricks, and Azure Synapse Analytics.
• Develop high-performance data transformation frameworks using PySpark, Python, and Spark SQL for large-scale distributed data processing.
• Optimize SQL queries, Spark jobs, and data workflows to improve performance, scalability, and cost efficiency.
• Lead data migration initiatives, including SQL Server migrations and modernization of legacy data platforms.
• Implement and maintain Delta Lake architecture, incremental data loading strategies, and enterprise data lake best practices.
• Collaborate with architects and cross-functional teams to design robust and scalable data models aligned with business and governance standards.
• Monitor and troubleshoot production pipelines, perform root-cause analysis, and implement preventive measures for recurring issues.
• Support CI/CD implementation and infrastructure automation for data engineering workflows.
• Mentor junior engineers and contribute to engineering standards, reusable frameworks, and technical best practices.
• Create and maintain technical documentation including architecture diagrams, pipeline documentation, and operational runbooks.
• Evaluate and recommend modern data engineering tools, frameworks, and optimization strategies.