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Strong Tier-1 brand, metro location, and broad mid-level data engineering skillset drive high competition.
Core data engineering skills are highly transferable across industries despite consulting context.
Mandatory technical skills (Python, SQL, cloud, big-data) but wide 0-8 year band makes filters moderately strict.
Develop and maintain high-performance data engineering applications and pipelines that support advanced analytics and machine learning at scale.
Collaborate with consulting teams and clients to understand business challenges and deliver tailored data-driven solutions globally.
Lead development of proprietary data engineering assets and advocate best practices including code quality, testing, and documentation.
Bachelor's or Master's degree in Computer Science, Informatics, Data Science, or a related quantitative discipline from a top academic program.
Proven experience designing and deploying large-scale cloud-native data solutions using Python and SQL on platforms like AWS, Azure, or GCP.
Experience with big data tools such as PySpark, Databricks, Snowflake, and building CI/CD pipelines with orchestration tools (e.g., Airflow).
Work Experience Required: Range from 0-8 years depending on level; Senior and Lead levels require strong mentoring skills and leadership experience.
Experienced in complex data engineering in consulting or client-facing environments, able to translate business challenges into scalable technical solutions.
Comfortable working internationally and collaborating with diverse teams across global clients, supporting project delivery and client satisfaction.
Demonstrates strong technical leadership, mentoring juniors on advanced engineering concepts, and advocating rigorous development and operational standards.