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Strong brand, metro location, and broad technical requirements increase candidate competition.
Data engineering skills are broadly transferable across industries, reducing background bias.
Multiple mandatory years, leadership experience, and specific cloud/data stack requirements make filtering stringent.
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Lead and grow a data engineering team focused on building and operating scalable, fault-tolerant, cloud-native enterprise data platforms including lakehouse, data warehouse, ETL/ELT pipelines.
Own the technical roadmap and delivery cadence for data platform capabilities that enable analytics, reporting, AI/ML readiness, and business decision-making.
Manage stakeholder alignment, resource planning, and operational stability while driving engineering best practices such as automation, CI/CD, testing, monitoring, and data governance.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics, or related technical field, or equivalent experience.
10+ years of hands-on data engineering experience, including production-grade pipelines and platform operation at scale.
3+ years of people leadership experience including hiring, coaching, mentoring, and performance management.
Strong proficiency in SQL, Python, Spark/PySpark, cloud data platforms (AWS, Snowflake, Databricks), and orchestration/streaming tools (Airflow, dbt, Kafka/Kinesis).
Experienced leader able to manage globally distributed teams and collaborate with cross-functional stakeholders including product, analytics, data science, and architecture.
Strategic thinker with practical expertise in data mesh, data product thinking, feature stores, and modern data architectures like lakehouses.
Skilled in managing platform reliability, cost optimization, automation, and infrastructure-as-code with containerization and cloud-native technologies.