Sr Data Engineer
Little Ferry, NJ, United States
Primary Responsibilities:
Lead the engineering team in building, scaling, and maintaining robust data solutions to support the firm's objectives.
Oversee the development of end-to-end data pipelines, including data acquisition, loading, and transformation, ensuring high reliability and efficiency.
Collaborate with business stakeholders to convert business needs into technical specifications, guiding projects from conception through to production.
Implement rigorous testing and monitoring practices to maintain superior data quality and integrity.
Mentor and develop junior team members, fostering a culture of excellence and continuous learning within the team.
Be willing to travel up to 20% of the time to participate in team work sessions and collaborative projects across locations. lined software engineering processes and best practices within the team and broader organization.
Requirements:
Education & Certificates
A bachelor's degree or higher in a STEM field, required
Concentration in Computer Science, Math, Physics or other engineering related field, preferred
Professional Experience
At least 7 years of experience in data engineering or a related discipline, with a proven track record of success.
Minimum of 2 years of being at a leadership role, managing tech teams or serving as a staff manager
Experience in the financial services or private equity industry, preferred
Competencies & Attributes
Expertise in Python and SQL, with a strong foundation in data manipulation and analysis.
Proficient 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 a plus, enhancing our data analysis and insight generation capabilities.
Demonstrated experience in designing and implementing complex data systems from the ground up.
Proficient in handling large-scale data projects, including acquisition, ETL processes, and information retrieval.
Familiarity with machine learning, especially in feature engineering for training models and inference.
Previous experience in a product development or financial services environment is highly desirable.
Excellent communication skills required, both verbal and written.
Due to the high volume of candidates, please be advised that only candidates selected to interview will be contacted by The Carlyle Group.
Company Profile:
The Carlyle Group (NASDAQ: CG) is a global investment firm with $382 billion of assets under management and more than half of the AUM managed by women, across 600 investment vehicles as of September 30, 2023. Founded in 1987 in Washington, DC, Carlyle has grown into one of the world's largest and most successful investment firms, with more than 2,200 professionals operating in 28 offices in North America, South America, Europe, the Middle East, Asia and Australia. Carlyle places an emphasis on development, retention and inclusion as supported by our internal processes and seven Employee Resource Groups (ERGs). Carlyle's purpose is to invest wisely and create value on behalf of its investors, which range from public and private pension funds to wealthy individuals and families to sovereign wealth funds, unions and corporations. Carlyle invests across three segments - Global Private Equity, Global Credit and Investment Solutions - and has expertise in various industries, including: aerospace, defense & government services, consumer & retail, energy, financial services, healthcare, industrial, real estate, technology & business services, telecommunications & media and transportation.
At Carlyle, we know that diverse teams perform better, so we seek to create a community where we continually exchange insights, embrace different perspectives and leverage diversity as a competitive advantage. That is why we are committed to growing and cultivating teams that include people with a variety of perspectives, people who provide unique lenses through which to view potential deals, support and run our business.
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