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Strong brand, metro location, mid-level generalist title, and broad tool requirements drive high competition.
Core data engineering skills are broadly transferable across industries despite platform-specific tool experience.
Explicit 3–7 years plus many mandatory platform and tool proficiencies increases filtering to high.
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Design, develop, and maintain scalable, high-performance distributed data pipelines and cloud-native data platforms supporting business intelligence, analytics, and machine learning.
Ensure data quality, security, and operational excellence by implementing monitoring, automation (audit and validation), and access controls.
Collaborate with agile and cross-functional teams, mentor junior engineers, and contribute to technical initiatives and documentation for data engineering solutions.
3 to 7 years of experience in architecting, designing, and building data engineering solutions and platforms.
Bachelor’s degree in Computer Science, Computer Engineering, or a relevant field.
Proven experience with distributed data processing frameworks (e.g., Apache Spark, Hadoop, Flink) and cloud services (especially AWS: S3, EC2, EMR, Lambda, RDS, DynamoDB, Redshift, Glue Catalog).
Expertise in designing/building batch and streaming data pipelines using Python, PySpark, Databricks or Snowflake, with advanced SQL and performance tuning skills.
Experienced in building and optimizing data platforms for large-scale, real-time streaming and batch processing within a cloud environment (preferably AWS).
Strong background in dimensional modeling, data pipeline automation (Airflow, Astronomer), and CI/CD practices (GitHub Actions, Jenkins).
Capability to work collaboratively in agile, cross-functional teams, with effective communication of technical concepts to technical and non-technical stakeholders.