Position Summary
Our client, a prestigious global private equity firm, is actively seeking a Senior Data Engineer to bolster its data science capabilities. This role is pivotal in advancing the firm's internal knowledge graph, ensuring a robust dataset foundation for diverse analytical and predictive use cases. The ideal candidate will possess extensive expertise in graph technologies, distributed computing, knowledge graphs, and machine learning applications.
Responsibilities
- Lead the engineering team in conceptualizing, building, and scaling robust data solutions to align with the firm's strategic objectives.
- Oversee the development of end-to-end data pipelines, encompassing data acquisition, loading, and transformation, with a focus on reliability and efficiency.
- Collaborate closely with business stakeholders to translate business requirements into technical specifications, guiding projects from inception to deployment.
- Implement rigorous testing and monitoring protocols to uphold superior data quality and integrity.
- Mentor and develop junior team members, fostering a culture of excellence and continuous learning.
- Be prepared to travel up to 20% of the time for team work sessions and collaborative projects across various locations.
Qualifications
Education & Certificates
- Bachelor's degree or higher in a STEM field is required.
- Concentration in Computer Science, Math, Physics, or related engineering field is preferred.
Professional Experience
- Minimum of 7 years of experience in data engineering or a related discipline, demonstrating a track record of success.
- At least 2 years of experience in a leadership role, managing technical teams or serving as a staff manager.
- Previous experience in the financial services or private equity industry is advantageous.
Competencies & Attributes
- Proficiency in Python and SQL, with a strong aptitude for data manipulation and analysis.
- Familiarity with Snowflake and dbt for data warehousing and transformation tasks.
- Experience with Databricks (PySpark) for large-scale data processing.
- Knowledge of graph databases and machine learning is desirable, enhancing data analysis and insight generation capabilities.
- Demonstrated expertise in designing and implementing complex data systems from the ground up.
- Skilled in managing large-scale data projects, including acquisition, ETL processes, and information retrieval.
- Familiarity with machine learning, particularly in feature engineering for model training and inference.
- Prior experience in product development or financial services environments is highly desirable.
- Excellent verbal and written communication skills are essential.
Note: Only shortlisted candidates will be contacted.
[Company Information: Details about Carlyle Group and its commitment to diversity and inclusion.]