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Mid-level, popular Data Engineer role with moderate brand and common required skills increases applicant competition.
Core data engineering skills are transferable across industries, though enterprise SaaS integrations add some domain specificity.
Explicit 5+ years plus required Snowflake/Python/cloud and CI/CD skills enforce strict technical filters.
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Design, build, and maintain batch or real-time enterprise-grade data pipelines leveraging AWS, Databricks, Snowflake technologies.
Collaborate with data architects, product owners, data scientists, and analysts to align data engineering solutions with long-term architecture and business requirements.
Provide technical leadership and mentorship on best practices in data engineering, CI/CD, code quality, and monitoring while driving pipeline performance and quality improvements.
5+ years of enterprise data engineering experience including building data integration pipelines with AWS, Databricks, Snowflake or equivalent cloud platforms.
Proficient in Python programming and at least one other language among SQL, Java, R, or Spark.
Experience with cloud database technologies (Azure, Snowflake, Databricks, Google Cloud, AWS Glue, Airflow).
Work Experience Required: 5+ years in enterprise data engineering. Notice period: Not explicitly mentioned in the JD.
Experienced in designing scalable data pipelines in agile Scrum environments with a focus on architectural alignment and operational excellence.
Strong background in data replication, integration, masking, and distributed data processing suited for enterprise-scale AI and analytics platforms.
Demonstrated ability to lead and influence data engineering teams and cross-functional stakeholders for delivering complex data solutions aligned with business strategy.