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Mid-level data engineer in a metro with broad cloud and Databricks skills increases applicant competition significantly.
Core data engineering skills (SQL, Python, Spark, cloud) transfer easily across industries; domain exposure is only a plus.
Explicit 6-8 years plus mandatory technologies (dbt, Databricks, PySpark, cloud, CI/CD) makes screening stringent.
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Lead design and implementation of scalable data pipelines for ingestion, transformation, and storage.
Provide technical guidance, enforce engineering best practices, and mentor data engineering teams.
Collaborate with cross-functional teams to deliver reliable, automated, and well-documented data solutions impacting global finance operations.
6-8 years of experience building and maintaining enterprise-scale data pipelines and platforms.
Proficiency in SQL, Python, PySpark, dbt, Databricks, Git, Azure DevOps, and cloud platforms (Azure or AWS).
Experience with CI/CD, infrastructure-as-code, testing, and automation practices.
Work Experience Required: 6-8 years in relevant data engineering roles. Notice period: Not explicitly mentioned in the JD.
Experienced leader in data engineering with proven ability to manage teams and projects while driving technical excellence.
Strong collaborator able to work effectively with data scientists, BI developers, and business stakeholders globally.
Familiarity or exposure to scientific datasets, FAIR data principles, or data governance is a plus but not mandatory.