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Common mid-level data engineer title with broad cloud and big-data requirements in metro increases candidate competition.
Data engineering skills like Spark, SQL, and cloud are broadly transferable across industries.
Explicit 5-year requirement and mandatory big-data, cloud, and language skills indicate high shortlisting strictness.
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Design, develop, and maintain scalable ETL pipelines to process large datasets using big data technologies (Apache Spark, Hadoop, Kafka).
Integrate and optimize structured and unstructured data from multiple sources ensuring quality, security, and consistency.
Deploy, manage, and monitor cloud-based data infrastructure on AWS, GCP, or Azure ensuring reliability and continuous improvement.
5 years of related experience with a Bachelor's degree or equivalent work experience.
Advanced proficiency in SQL and experience with relational and NoSQL databases (PostgreSQL, MySQL, MongoDB).
Strong programming skills in Python, Java, or Scala focused on data processing and automation.
Hands-on experience with big data frameworks (Apache Spark, Hadoop, Kafka) and cloud platforms (AWS Redshift, Google BigQuery, Azure Synapse).
Experienced building and optimizing large-scale ETL pipelines with deep expertise in data modeling and warehousing.
Comfortable working across teams including data scientists, analysts, and software engineers with strong communication skills.
Proficient in cloud data infrastructure deployment and automation, with a focus on scalability, performance, and governance.