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Strong employer brand, metro location, and senior data role balanced by required certification and leadership experience.
Role requires specialized Databricks, Spark, and AWS skills but core data engineering skills remain transferable across industries.
Mandatory 8-13 years, AWS certification, leadership and Databricks/Spark experience create stringent filters.
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Lead and mentor a team of data engineers to design, build, and maintain scalable, fault-tolerant data pipelines and analytics solutions primarily using Databricks (Spark, Delta Lake) and cloud platforms (AWS).
Oversee data governance, data quality, and performance monitoring across data pipelines with emphasis on automation, self-healing systems, and cost-effective solutions.
Collaborate with business and technical stakeholders to align data architecture with product requirements and drive Agile/SAFe methodologies for project delivery.
8 to 13 years of experience in Computer Science, IT, or related field.
Experience managing a team of data engineers and architecting ETL data and analytics solutions from multiple source systems.
Proficiency with Python, PySpark, SQL; experience with Apache Spark, Apache Airflow, and cloud services (AWS required, GCP/Azure optional).
AWS Certified Data Engineer certification (must-have).
Experienced in leading data engineering teams in large-scale, cloud-based environments with strong emphasis on automation, CI/CD, and DataOps.
Strong background in data architecture, dimensional modeling, and implementation of robust data governance and compliance practices (e.g., GDPR, CCPA).
Capable of driving Agile/SAFe team processes, mentoring engineers, and collaborating cross-functionally with business and technical teams to deliver business-aligned data solutions.