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TravelersSenior Data Engineer
Senior Associate Data Engineer contributing to Travelers' analytics landscape by building and operationalizing data solutions. Collaborating with teams to ensure reliable data delivery across the enterprise.
About the role
- Drive modernization from legacy and on-prem systems to modern, cloud-native, and hybrid data platforms.
- Architect and lead the development of a Multi-Agent ETL Platform for batch and event streaming, integrating AI agents to autonomously manage ETL tasks such as data discovery, schema mapping, and error resolution.
- Define and implement data ingestion, transformation, and delivery pipelines using scalable frameworks (e.g., Apache Airflow, Nifi, dbt, Spark, Kafka, or Dagster).
- Leverage LLMs, and agent frameworks (e.g., LangChain, CrewAI, AutoGen) to automate pipeline management and monitoring.
- Ensure robust data governance, cataloging, versioning, and lineage tracking across the ETL platform.
- Define project roadmaps, KPIs, and performance metrics for platform efficiency and data reliability.
- Establish and enforce best practices in data quality, CI/CD for data pipelines, and observability.
- Collaborate closely with cross-functional teams (Data Science, Analytics, and Application Development) to understand requirements and deliver efficient data ingestion and processing workflows.
- Establish and enforce best practices, automation standards, and monitoring frameworks to ensure the platform’s reliability, scalability, and security.
- Build relationships and communicate effectively with internal and external stakeholders, including senior executives, to influence data-driven strategies and decisions.
- Continuously engage and improve teams’ performance by conducting recurring meetings, knowing your people, managing career development, and understanding who is at risk.
- Oversee deployment, monitoring, and scaling of ETL and agent workloads across multi cloud environments.
- Continuously improve platform performance, cost efficiency, and automation maturity.
Requirements
- Hands-on experience in data engineering, data platform strategy, or a related technical domain.
- Proven experience leading global data engineering or platform engineering teams.
- Proven experience in building and modernizing distributed data platforms using technologies such as Apache Spark, Kafka, Flink, NiFi, and Cloudera/Hadoop.
- Strong experience with one or more data pipeline tools (Nifi, Airflow, dbt, Spark, Kafka, Dagster, etc.) and distributed data processing at scale.
- Experience building and managing AI-augmented or agent-driven systems will be a plus.
- Proficiency in Python, SQL, and data ecosystems (Oracle, AWS Glue, Azure Data Factory, BigQuery, Snowflake, etc.).
- Deep understanding of data modeling, metadata management, and data governance principles.
- Proven success in leading technical teams and managing complex, cross-functional projects.
- Passion for staying current in a fast-paced field with proven ability to lead innovation in a scaled organization.
- Excellent communication skills, with the ability to tailor technical concepts to executive, operational, and technical audiences.
- Expertise and ability to lead technical decision-making considering scalability, cost efficiency, stakeholder priorities, and time to market.
- Proven track record of leading high-performing teams with experience leading and coaching director level reports and experienced individual contributors.
- Advanced degree in Data Science, Computer Science, Information Technology, Business Administration, or a related field. Equivalent experience will also be considered.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
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