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Mid-level data engineer, metro location, broadly required skills, and common title increase applicant competition.
Data engineering skills are broadly transferable across industries and platforms.
Explicit 4+ years requirement plus mandatory Python, Spark, SQL, and cloud skills enforces strict filtering.
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Own and scale backend data infrastructure powering real-time optimization, analytics, and ML applications.
Design, implement, and maintain robust, scalable batch and real-time data pipelines using Spark and modern data tools.
Drive architectural decisions regarding distributed data processing, pipeline reliability, and scalability.
4+ years experience in backend data engineering or infrastructure-focused software development.
Proficiency in Python and SQL; experience designing scalable, low-latency batch and streaming data pipelines.
Experience with cloud-native environments, especially GCP or AWS, including containerization (Docker, Kubernetes).
Degree in Computer Science, Engineering, or related field.
Experienced in building and operating data platforms including pipelines, data lakes, and developer tooling in production environments.
Skilled in using orchestration tools like Airflow, modern CI/CD practices, and Spark for distributed data processing at scale.
Able to collaborate with cross-functional teams to translate complex data requirements into scalable solutions.