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High due to strong Tier-1 brand, metro location, and a visible mid-level analytics engineering role.
Low because core skills (Databricks, AWS, dimensional modeling, SQL/Python) are highly transferable across industries.
High because 6+ years is required plus mandatory Databricks, Delta Lake, AWS, and dimensional modeling skills.
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Design, build, and own scalable analytics-layer transformations and data models on Databricks for commercial analytics and reporting.
Set standards, mentor junior developers, and lead best practices in data modeling, Databricks architecture, and AWS resource usage to optimize performance, scalability, and cost.
Partner with analysts, data scientists, and business stakeholders to define metrics, data models, and governed datasets powering dashboards and advanced analytics, while communicating technical decisions clearly to business audiences.
6+ years of hands-on experience in analytics engineering or related roles with significant production experience on Databricks and Delta Lake.
Expertise in Databricks (notebooks, jobs, Unity Catalog), Delta Lake, and AWS services (S3, IAM, Glue, Lambda) supporting Databricks Lakehouse.
Strong data modeling skills with dimensional/Kimball-style star and snowflake schemas, conformed dimensions, fact tables, and slowly changing dimensions.
Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Statistics/Mathematics, or related field (or equivalent practical experience).
Senior hands-on analytics engineer experienced in architecting and optimizing commercial data products at scale on Databricks and AWS.
Comfortable mentoring junior developers through pairing and code review while actively contributing to shared team deliverables.
Skilled at translating complex business requirements into governed, performant, and reusable analytical data models, and explaining technical concepts effectively to business stakeholders.