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Strong brand, metro location, mid-level generalist data role with broad tech requirements.
Core data engineering skills transfer across industries, but supply-chain domain context adds moderate specialization.
Explicit 5-7 years plus specific Databricks, Snowflake, Spark, Terraform, and cloud requirements.
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Design, develop, and maintain scalable data solutions, including data pipelines and APIs to support enterprise-wide analytics needs.
Implement best practices in software development, data governance, and platform observability to ensure data quality and reliability.
Collaborate with cross-functional teams and mentor junior engineers to deliver high-impact data engineering solutions.
5-7 years of software development experience with expertise in distributed systems, cloud-native architectures, and data platforms.
Proficient in Python, SQL, and cloud platforms like AWS, Azure, or GCP with hands-on experience in Databricks, Snowflake, Apache Spark, and orchestrators like Apache Airflow.
Experience managing cloud infrastructure programmatically using Terraform or AWS CloudFormation.
Work Experience Required: 5-7 years in relevant software development and data engineering roles.
Experienced in building scalable, API-first services and robust modern data pipelines adhering to Lakehouse architecture and Data Mesh principles.
Operates well in fast-paced, collaborative environments with cross-functional teams, showing capability to mentor junior engineers.
Preferably has exposure to AI/ML concepts, generative AI, LLMs, and responsible AI practices, enhancing data platform capabilities.