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Strong brand, common Data Engineer title, mid-level profile, and metro location increase applicant competition.
Core data engineering skills (ETL, SQL, pipelines) are broadly transferable across industries.
Requires specific SQL, RDBMS and ETL skills plus Databricks familiarity, so moderately strict filtering applies.
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Design, implement, and maintain enterprise ETL processes and data pipelines supporting global clients in various industry sectors.
Optimize data processing using SQL and related database technologies to develop scalable, efficient code for handling large datasets.
Collaborate with cross-functional teams to deliver timely, accurate, and robust data solutions while adhering to internal policies and external regulations.
Bachelor's degree in a quantitative field such as Computer Science, Statistics, Econometrics, Engineering, Mathematics, or Operations Research; Master's preferred.
Experience as a Data Engineer or similar role with strong understanding of data engineering concepts and methodologies.
Proficient in SQL and Microsoft SQL Server; familiarity with ETL frameworks and database design for scalable data solutions.
Work Experience Required: Not explicitly mentioned in the JD.
Skilled in optimizing large-scale data processing and building scalable data pipelines across multiple projects and clients.
Comfortable working in a global, multi-time-zone environment with collaborative teams including senior engineers.
Demonstrates critical thinking and problem-solving skills with an eagerness to learn emerging developer productivity tools and best practices.