Data Engineer

About DeepSource: DeepSource builds cutting-edge developer tools and automation platforms trusted by elite software teams worldwide to discover, track, and fix security vulnerabilities, code anti-patterns, and performance bottlenecks automatically. Moving at hyper-scale, our infrastructure processes millions of source lines daily. We prioritize deep technical precision, automation compliance, and developer workflow optimization to help companies build and ship software safely, reliably, and with absolute confidence.

Position Overview

We are seeking a highly analytical, systems-oriented Data Engineer (L2) to design, build, optimize, and maintain the cloud-native data architecture and big data pipelines required for our organization data demands. In this mid-to-senior technical track, you will own the end-to-end delivery of enterprise data workflows, leveraging the Microsoft Azure and Databricks ecosystems. From driving high-priority SQL Server migration initiatives to performance tuning distributed datasets, you will ensure our computational solutions are robust, scalable, and fully optimized. Your evaluation metrics will lean directly on skills mapped within professional lakehouse certification standards.

Key Responsibilities

• Big Data Architecture Ownership: Design, develop, deploy, and maintain scalable cloud-based data solutions and high-volume data architectures.
• Distributed Processing Pipelines: Construct and scale programmatic data integration networks and analytical layers utilizing Python, Pandas, and PySpark framework modules.
• Azure Analytics Orchestration: Deliver advanced, hands-on data workflow orchestrations natively using Databricks, Azure Synapse Spark, and Azure Data Factory.
• Legacy Database Modernization: Lead complex data migration projects transferring on-premise or traditional SQL Server infrastructure clusters cleanly into cloud lakehouse systems.
• SQL Performance Engineering: Author, audit, and refactor complex analytical queries, enforcing rigorous query optimization, execution indexing, and database performance tuning.
• Operational Process Automation: Establish highly automated enterprise data workflows independently, monitoring pipeline thresholds, connection pool limits, and ingestion lag.

Required Skills & Qualifications

• 5+ years of verified professional history executing complex data engineering workflows, big data analytics, or platform infrastructure management.
• Master-level technical competency deploying data pipelines using Python, Pandas, and distributed PySpark runtimes.
• Advanced, hands-on production engineering experience manipulating cloud clusters inside Databricks and Azure Synapse Spark.
• Strong background configuring end-to-end ETL/ELT pipelines utilizing Azure Data Factory.
• Demonstrated tracking history executing database migrations, explicitly handling SQL Server architectures and complex SQL performance tuning.
• Technical knowledge structures closely aligned with the professional evaluation matrices of the **Databricks Certified Data Engineering Professional** standard.
• Location Context: 100% remote-first operational infrastructure flexibility open exclusively to qualified technical engineers residing within Egypt, Jordan, India, Pakistan, Saudi Arabia, or Bangladesh.

Preferred Strategic Indicators (Nice to Have)

• Familiarity with Infrastructure as Code (IaC) architectures using Terraform for automated Azure infrastructure provisioning and cloud deployment workflows.
• Prior experience setting up cloud data warehouse topologies or managing multi-tenant analytical databases.

What We Offer

• The exceptional professional runway to completely own and tune the big data pipelines powering an innovative, scale-up developer ecosystem.
• Highly competitive global compensation package calibrated accurately based on engineering depth and technical evaluation results.
• Complete work-from-home remote parameters matching your target local timezone framework.
• A highly collaborative, low-friction environment optimized for engineers who thrive on deep technical autonomy and continuous learning.