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Protocol Intelligence
Data-driven signals on your job's competitivenessRemote, mid-level data engineer title with broad cloud and pipeline requirements increases applicant competition.
Core data engineering skills are transferable, but platform-specific tools raise domain sensitivity.
Explicit 4+ years plus multiple mandatory technologies and cloud platform requirements raise filtering strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain scalable, secure, and reliable data pipelines using platforms like Spark, Databricks, Airflow, and Snowflake.
Develop ETL/ELT processes to ingest data from various structured and unstructured sources, enabling high-quality analytics and real-time insights.
Collaborate with data scientists, analysts, and engineers to create data models and ensure reliable feature delivery in an agile environment, including writing maintainable Python code with tests.
Minimum Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
4+ years of hands-on experience in data engineering or backend software development.
Proficiency in Python, SQL, and experience with data pipeline orchestration tools such as Apache Airflow, Luigi, or Prefect.
Experience with relational databases (RDS, MySQL, PostgreSQL), message queues (Apache Kafka or RabbitMQ), and cloud data platforms (AWS Redshift, GCP BigQuery, Snowflake, Databricks).
Ideal Candidate Profile
Experienced in designing and operating distributed data systems and data warehouses with strong data modeling expertise.
Comfortable collaborating across functions including data science and software engineering, and working in agile teams responsible for end-to-end product delivery.
Familiar with asynchronous messaging systems and DevOps practices like CI/CD and containerization, and ideally interested in AI/ML data integration.