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Tier-1 brand, metro location, broad data/cloud skillset, mid-level range, and popular Data Engineer title.
Data engineering skills are transferable, but consulting context and client-facing requirements raise domain specificity.
Mandatory cloud, Python, SQL, big-data skills and top‑tier academic requirement indicate strict screening.
Develop and monitor high-performance data engineering applications enabling rapid deployment of machine learning frameworks and advanced analytics at scale.
Collaborate directly with clients and consulting teams to understand business challenges and design/manage large-scale data pipelines and ETL processes.
Lead data engineering efforts including best practices advocacy, architecture design, proprietary asset development, documentation, and supporting proposal development.
Bachelor's or master’s degree in Computer Science, Informatics, Data Science, or related quantitative discipline from a top academic program.
Mandatory proficiency in Python and strong SQL skills; experience with cloud platforms (AWS, Azure, or GCP) and big data frameworks (PySpark, Databricks, Snowflake, DBT).
Prior experience designing and deploying large-scale data solutions with CI/CD pipeline integration and test-driven development.
Work Experience Required: Ranges from 0 to 8 years; notice period not explicitly mentioned in the JD.
Experienced with cloud-native data applications and modern data engineering tools capable of handling big data at scale.
Capable of independently managing workload, prioritizing tasks in fast-paced environments, and mentoring junior engineers on advanced data engineering.
Ready to engage in client-facing roles internationally and work collaboratively across diverse teams and stakeholders to deliver impactful solutions.