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Tier-1 brand, common mid-level data engineer title, and 3–6 years experience increase candidate competition.
Core data engineering skills (Spark, Azure, Airflow) are highly transferable across industries.
Multiple mandatory technologies plus explicit 4+ years requirement enforce strict screening.
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Design, develop, and maintain scalable data applications and pipelines using Python, PySpark, Scala Spark on Azure Cloud platforms.
Manage and schedule data workflows with Apache Airflow, ensuring secure handling of secrets via Azure Key Vault.
Support production activities including monitoring, troubleshooting, performance optimization, and application migration.
Bachelor's or Master's degree in Computer Science, IT, or equivalent.
More than 4 years of relevant work experience in data engineering.
Hands-on experience with Apache Spark, Python, Azure services (Azure Data Lake, Databricks, Data Factory, Key Vault), and Apache Airflow.
Proficient in SQL/PLSQL, Shell scripting, and familiarity with cloud CI/CD tools like Jenkins or GitHub Actions.
Experienced in building and optimizing big data pipelines and cloud-native architectures on Azure.
Comfortable working in fast-paced, team-oriented environments with strong analytical skills.
Able to leverage AI-assisted development tools and adhere to software development best practices in collaborative settings.