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Mid-level role in a metro with common big-data skills and known employer increases applicant competition.
Core data engineering skills are highly transferable across industries.
Explicit 2–5 years plus mandatory PySpark, Python and SQL narrows candidate pool.
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Build and maintain scalable data solutions including data lakes, warehouses, ETL/ELT pipelines, and analytics platforms.
Develop and automate low-latency, high-velocity data pipelines using technologies like Apache Kafka, Spark Streaming, and Apache Airflow.
Implement data governance, quality standards, and ensure security and compliance across data solutions.
2-5 years of experience in Data Engineering or a related field.
Proficient in Python, PySpark, and SQL (mandatory).
Experience with big data technologies such as Spark, Hive, Hadoop, Airflow, Oozie, HBase, or MapReduce.
Experience working in Linux environments, shell scripting, Git, relational databases, and data pipeline development.
Experienced in building and automating complex data pipelines for real-time data processing and integration.
Comfortable working with a wide range of big data tools and orchestration frameworks in a global, fast-paced environment.
Able to ensure compliance, data governance, and security while exploring and implementing new data technologies aligned with organizational standards.