Full Stack Sr. Data Engineer

EXL (NASDAQ: EXLS) is a global analytics and digital solutions company that partners with clients to improve business outcomes and unlock growth. Bringing together deep domain expertise with robust data, powerful analytics, cloud, and AI, we create agile, scalable solutions and execute complex operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media, and retail, among others. Focused on creating value from data for driving faster decision-making and transforming operating models, EXL was founded on the core values of innovation, collaboration, excellence, integrity, and respect. Headquartered in New York, our team is over 50,000 strong, with more than 50 offices spanning six continents.

EXL Analytics provides data-driven, action-oriented solutions to business problems through cutting edge analytics techniques and technologies mixed with a consultative approach. Leveraging proprietary methodology and best-of-breed technology, EXL Analytics takes an industry-specific approach to transform our clients’ decision making and embed analytics more deeply into their business processes.

For more information, visit www.exlservice.com.

Job Overview:

We are seeking an experienced and visionary Senior Full-Stack Data Engineer to lead the architecture, development, and optimization of a next-generation data platform. This is a critical role for an individual with over 15 years of deep data engineering expertise, capable of driving technical direction, mentoring team members, and delivering high-impact solutions in a fast-paced project environment..

Key Responsibilities:

• Platform Strategy & Leadership
• Technical Direction: Define and champion the architectural roadmap and best practices for our end-to-end data pipelines, ensuring scalability, reliability, and security across the platform.
• Team Mentorship & Project Velocity: Act as a primary technical mentor, guiding a team of engineers, conducting code reviews, and aggressively driving the project timeline to ensure rapid delivery of data products.
• Stakeholder Collaboration: Partner with Data Scientists, Analysts, and business stakeholders to translate complex requirements into robust, production-ready data solutions.
• Collaboration with Data Scientists and ML Engineers:Data Accessibility, Support for Model Development, Data Quality Assurance
• Data Pipeline Development & Management
• Ingestion & Transformation: Design, build, and optimize high-volume data ingestion and transformation jobs using tools like dbt Core, AWS Glue, or Flexter, ensuring data quality and integrity.
• Workflow Orchestration: Develop and maintain sophisticated data pipelines using orchestrators such as Dagster or Talend, focusing on modularity and reusability.
• Streaming & Real-time Integration: Implement and manage real-time data flows utilizing Confluent platforms or native AWS streaming services (e.g., Kinesis) for immediate data availability.
• Data Security and Privacy: Data Anonymization, Compliance with Regulations
• Be well versed with DataOps and DevOps fundamentals
• Assist and drive the Data Ecosystem Management & Monitoring
• Open Table Formats & Management: Implement and maintain the Iceberg open table format, utilizing tools like Upsolver (Talend Open Lakehouse) for efficient schema evolution and data management.
• Compute Engine Optimization: Optimize query performance and cost efficiency across our primary compute engines: Snowflake, Amazon Redshift, and AWS Athena.
• Observability & Monitoring: Integrate comprehensive monitoring and observability into all pipelines using Splunk to ensure high availability, rapidly identify bottlenecks, and troubleshoot production issues

Candidate Profile:

• 15+ Years of hands-on, progressive experience in Data Engineering, Data Architecture, or a closely related Full-Stack Data role
• Deep conceptual understanding of core data engineering principles, including data modeling (e.g., Dimensional, Data Vault), ETL/ELT patterns, and metadata management
• Proven track record of building and managing petabyte-scale data infrastructure in a cloud-native environment
• Insurance industry experience preferred but not mandatory
• Tools:
• Cloud Environment: AWS (S3, IAM, VPC, etc.)
• Experience with Talend, dbt Core, Iceberg, AWS Glue Catalog, Snowflake, Redshift, Athena, Splunk, AWS streaming services, Git
• Strong SQL, Pyspark and Python