Principal Data Engineer

Role Overview: As a Data Engineer at KKR, your primary responsibility will be to design, build, and maintain scalable data pipelines and platforms to support analytics, reporting, and advanced data science use cases. You will collaborate closely with data analysts, data scientists, and engineering teams to ensure that data is reliable, accessible, and production-ready. This role operates in a 4-day in-office and 1-day flexible work arrangement. Key Responsibilities: - Design, develop, and maintain robust data pipelines for batch and real-time processing - Build and manage data ingestion frameworks from multiple structured and unstructured data sources - Develop and optimize ETL / ELT workflows, including building modular, tested, and version-controlled transformations using dbt - Ensure data quality, integrity, availability, and security across platforms - Collaborate with analytics, data science, and product teams to understand data requirements - Model data for analytics and reporting (dimensional modeling, star/snowflake schemas) - Monitor and troubleshoot data pipelines and resolve performance or reliability issues - Implement best practices for data governance, documentation, and lineage Qualifications: - 13+ years of experience with Bachelors degree in Computer Science, Engineering, or related field (or equivalent experience) - Strong proficiency in SQL and experience with relational and NoSQL databases - Hands-on experience with data processing frameworks (Spark, Flink, or similar) - Proficiency in Python, Java, or Scala - Experience building data pipelines using workflow orchestration tools (Airflow, Dagster, etc.) - Hands-on experience with dbt (data build tool) for developing, testing, and documenting analytics-ready data models - Solid understanding of data architecture, data modeling, warehousing, and lakehouse concepts - Familiarity with version control systems (Git) and CI/CD practices Preferred Qualifications: - Experience with cloud data platforms (AWS, Azure, or GCP) - Hands-on experience with data warehouses and lakehouses (Snowflake, BigQuery, Redshift, Databricks) - Knowledge of streaming platforms (Kafka, Kinesis) - Experience implementing dbt at scale, including macros, packages, exposures, and CI/CD integration for dbt projects - Exposure to data governance, security, and compliance standards - Experience working in agile development environments - Understanding of data observability and monitoring tools - Asset management domain experience Note: KKR is an equal opportunity employer and provides reasonable accommodations as required by applicable laws. Individuals seeking accommodations should email Benefits@kkr.com. Applicants in Massachusetts are protected from lie detector tests as a condition of employment. Role Overview: As a Data Engineer at KKR, your primary responsibility will be to design, build, and maintain scalable data pipelines and platforms to support analytics, reporting, and advanced data science use cases. You will collaborate closely with data analysts, data scientists, and engineering teams to ensure that data is reliable, accessible, and production-ready. This role operates in a 4-day in-office and 1-day flexible work arrangement. Key Responsibilities: - Design, develop, and maintain robust data pipelines for batch and real-time processing - Build and manage data ingestion frameworks from multiple structured and unstructured data sources - Develop and optimize ETL / ELT workflows, including building modular, tested, and version-controlled transformations using dbt - Ensure data quality, integrity, availability, and security across platforms - Collaborate with analytics, data science, and product teams to understand data requirements - Model data for analytics and reporting (dimensional modeling, star/snowflake schemas) - Monitor and troubleshoot data pipelines and resolve performance or reliability issues - Implement best practices for data governance, documentation, and lineage Qualifications: - 13+ years of experience with Bachelors degree in Computer Science, Engineering, or related field (or equivalent experience) - Strong proficiency in SQL and experience with relational and NoSQL databases - Hands-on experience with data processing frameworks (Spark, Flink, or similar) - Proficiency in Python, Java, or Scala - Experience building data pipelines using workflow orchestration tools (Airflow, Dagster, etc.) - Hands-on experience with dbt (data build tool) for developing, testing, and documenting analytics-ready data models - Solid understanding of data architecture, data modeling, warehousing, and lakehouse concepts - Familiarity with version control systems (Git) and CI/CD practices Preferred Qualifications: - Experience with cloud data platforms (AWS, Azure, or GCP) - Hands-on experience with data warehouses and lakehouses (Snowflake, BigQuery, Redshift, Databricks) - Knowledge of streaming platforms (Kafka, Kinesis) - Experience implementing dbt at s