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Well-known employer, mid-level generalist data role, metro location, and broad cloud/big-data skillset increase competition.
Core data engineering skills (ETL, Spark, AWS) are broadly transferable across industries, so fit sensitivity is low.
Explicit 3+ years and numerous mandatory AWS, big-data, and DevOps skills create stringent technical filters.
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Build and maintain scalable ETL/ELT data pipelines and platforms leveraging AWS services such as Glue, Lambda, Step Functions, and Redshift.
Manage large structured and unstructured datasets ensuring data partitioning, indexing, lifecycle management, security, and compliance with governance policies.
Collaborate within Agile teams to optimize performance, monitor pipeline health, and translate complex business requirements into production-grade data solutions.
Minimum 3+ years professional experience in data engineering or software engineering with strong data focus.
Proficiency with AWS ecosystem including S3, Glue, Redshift, Lambda, Kinesis, Lake Formation, and DevOps tools like CloudFormation or Terraform.
Strong programming skills in Python and SQL; experience with big data tools such as Apache Spark, Databricks, Hadoop, Kafka.
Bachelor's degree in Engineering, Computer Science, or equivalent experience required.
Demonstrated ability to deliver complex, governed data pipelines integrating multiple data sources at scale using AWS technologies.
Experience working cross-functionally in Agile environments with data science, analytics, and product teams to align solutions with business needs.
Strong understanding of data security, compliance (GDPR), and automation to optimize efficiency and maintain secure SDLC practices.