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Mid-level data engineer title, metro location, and popular generalist skillset produce high competition.
Tool- and cloud-specific requirements moderately limit cross-industry transferability.
Explicit 3+ years plus mandatory Databricks, Azure, and PySpark skills make shortlisting highly strict.
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Lead client data engineering initiatives, ensuring optimal data delivery architecture across projects.
Build, maintain, and monitor complex data solutions and pipelines leveraging Azure and cloud data platforms.
Mentor and grow the data engineering team, assisting in defining and supporting client data solution delivery.
3+ years of experience as a Data Engineer.
Proficient in PySpark, Python, SQL; experience with Azure Data Lake, Azure Data Factory, Azure Databricks.
Experience in designing data models and building scalable data pipelines using big data tools like Databricks, Spark, Snowflake, Airflow, Kafka.
Graduate degree in Computer Science, Statistics, Informatics, Information Systems or equivalent.
Experienced in leading data engineering efforts and managing cross-functional data infrastructure needs.
Skilled in developing and optimizing big data architectures in dynamic, client-facing environments.
Strong technical background in cloud data platforms (Azure/AWS) and implementing scalable ETL processes aligned with business requirements.