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Protocol Intelligence
Data-driven signals on your job's competitivenessMetro-based, mid-seniority Data Engineer with common stack—moderate applicant competition.
Skills transfer across industries but Databricks/lakehouse experience moderately favors data-focused employers.
Explicit 6–8 years plus 5+ years Databricks and cloud tech mandates strict filtering.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and optimize scalable data pipelines and Lakehouse platforms using Databricks, PySpark, Python, SQL, and cloud technologies.
Lead performance tuning, Spark optimization, and implement engineering standards for data solutions focusing on scalability, reliability, and cost-effectiveness.
Drive data quality, governance, security practices and contribute to architectural decisions in large-scale, cloud-native data environments.
Minimum Requirements
6–8 years of Data Engineering experience, including 5+ years of hands-on Databricks experience.
Bachelor's or Master's degree in Computer Science, Engineering, Information Systems or related field.
Strong expertise in Python, PySpark, SQL, distributed data processing, and experience with AWS services like S3, EMR, Glue, Lambda, ECS/EKS, Redshift, IAM.
Experience with Lakehouse architecture, Spark optimization, scalable data modeling, CI/CD, automated testing, and cloud-native data platforms.
Ideal Candidate Profile
Experienced in designing and operating large-scale cloud data platforms with deep expertise in Databricks and Spark performance tuning.
Able to lead technical initiatives and architectural decisions focusing on modern DataOps and Data Product practices.
Comfortable working in Agile, globally distributed, cross-functional teams with strong stakeholder management and problem-solving skills.