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Mid-level Data Engineer role at a known brand with broad sought-after skills, so highly competitive.
Core data engineering skills (ETL, SQL, cloud) transfer easily across industries, so low sensitivity.
Explicit 6–10 years plus many mandatory technologies (Snowflake, Python, cloud, DevSecOps, IaC) increases strictness.
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Architect, build, and optimize end-to-end scalable ELT/ETL data pipelines and enterprise-grade data platforms primarily using Python, SQL, and Snowflake.
Lead technical decision-making, oversee cloud-native data solutions (AWS/Azure/GCP), and implement DevSecOps best practices, including CI/CD, infrastructure as code, and automated data quality testing.
Mentor junior engineers and collaborate cross-functionally with Product, Data Science, and Business teams to improve data quality, performance, and reliability.
6–10 years of professional experience in Data Engineering.
Strong expertise in Python, advanced SQL skills, and hands-on experience managing Snowflake at enterprise scale.
Experience with cloud platforms (AWS, Azure, or GCP), CI/CD tools, Infrastructure as Code (Terraform/Bicep), and DevSecOps practices.
Work Experience Required: 6–10 years in Data Engineering. Notice Period: Not explicitly mentioned in the JD.
Experienced in designing and managing high-scale data warehousing and modeling using dimensional modeling, Kimball, and Data Vault patterns.
Proficient in implementing and optimizing complex data pipelines with a strong focus on security, automation, and cost optimization.
Demonstrated leadership in technical mentoring, cross-team collaboration, and driving engineering excellence in data platform environments.