We are seeking a Delivery Lead to manage the end-to-end delivery of data platform projects, including solutions leveraging Generative AI and modern data engineering architectures. This role requires strong coordination across cross-functional teams of Data Engineers, Machine Learning Engineers, Architects, and platform specialists to ensure successful implementation and business
Base Compensation Range: 100,000 - 125,000
The posted range is the hiring range for this role - a subset of the broader range available to employees over time - and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate''s skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits
Bachelor''s in engineering or related field
Responsibilities:
- Oversee and drive the delivery of end-to-end projects focused on analytical applications and data solutions.
- Provide technical leadership and mentorship to data engineering teams, guiding them on complex challenges and best practices.
- Collaborate with business and technical stakeholders to gather requirements, design solutions, and manage project deliverables.
- Utilize Generative AI frameworks like OpenAI, Hugging Face, or Google Vertex AI to design, deploy, and integrate cutting-edge AI/ML solutions.
- Manage cross-functional collaboration with Business Data Analysts (BDAs), ensuring alignment between data engineering and business goals.
- Take ownership of release planning, change management, and stakeholder engagement to ensure project success.
- Ensure adherence to Agile practices, facilitating sprint planning, retrospectives, and delivery timelines.
Technical Expertise:
- Framework and Foundational Services: Expertise in building modular, scalable, and reusable frameworks for data integration, data quality validation, and pipeline orchestration.
- Generative AI Integration: Hands-on experience with LLM (Large Language Models), Agentic AI, and integration with APIs from platforms like OpenAI, Hugging Face, and AWS AI services.
- Data Engineering: Strong background in Snowflake, SQL, DBT, and PySpark, with advanced knowledge of query optimization, CDC (Change Data Capture), and data transformation.
- Cloud Platforms: Extensive experience with AWS (Glue, EMR, S3, Lambda, Redshift), Azure Data Factory, and cloud-native architectures.
- Application Development: Expertise in developing event-driven and microservices-based architectures using Java, Python, and modern frameworks.
- CI/CD & Automation: Proficient in building CI/CD pipelines with tools like Jenkins, GitLab, and Terraform for infrastructure-as-code automation.
- Big Data & Analytics: Proven experience in managing Big Data platforms and implementing solutions for large-scale data ingestion, storage, and processing.
- Security & Compliance: Implementation of security best practices, including OWASP guidelines, RBAC, and vulnerability detection/remediation.
Must Have:
- Proven experience in building scalable data engineering frameworks and foundational services to support enterprise-wide analytics initiatives.
- Hands-on expertise in Snowflake, Spark, and AWS Glue, with knowledge of event-driven architectures and streaming data solutions.
- Strong leadership experience in managing teams and delivering complex projects in Agile environments with leading team size of more than 20 members
- Proficiency in integrating Generative AI solutions into existing platforms and workflows.
- Excellent communication and stakeholder management skills to collaborate across diverse teams.
Responsibilities:
- Oversee and drive the delivery of end-to-end projects focused on analytical applications and data solutions.
- Provide technical leadership and mentorship to data engineering teams, guiding them on complex challenges and best practices.
- Collaborate with business and technical stakeholders to gather requirements, design solutions, and manage project deliverables.
- Utilize Generative AI frameworks like OpenAI, Hugging Face, or Google Vertex AI to design, deploy, and integrate cutting-edge AI/ML solutions.
- Manage cross-functional collaboration with Business Data Analysts (BDAs), ensuring alignment between data engineering and business goals.
- Take ownership of release planning, change management, and stakeholder engagement to ensure project success.
- Ensure adherence to Agile practices, facilitating sprint planning, retrospectives, and delivery timelines.
Technical Expertise:
- Framework and Foundational Services: Expertise in building modular, scalable, and reusable frameworks for data integration, data quality validation, and pipeline orchestration.
- Generative AI Integration: Hands-on experience with LLM (Large Language Models), Agentic AI, and integration with APIs from platforms like OpenAI, Hugging Face, and AWS AI services.
- Data Engineering: Strong background in Snowflake, SQL, DBT, and PySpark, with advanced knowledge of query optimization, CDC (Change Data Capture), and data transformation.
- Cloud Platforms: Extensive experience with AWS (Glue, EMR, S3, Lambda, Redshift), Azure Data Factory, and cloud-native architectures.
- Application Development: Expertise in developing event-driven and microservices-based architectures using Java, Python, and modern frameworks.
- CI/CD & Automation: Proficient in building CI/CD pipelines with tools like Jenkins, GitLab, and Terraform for infrastructure-as-code automation.
- Big Data & Analytics: Proven experience in managing Big Data platforms and implementing solutions for large-scale data ingestion, storage, and processing.
- Security & Compliance: Implementation of security best practices, including OWASP guidelines, RBAC, and vulnerability detection/remediation.
Must Have:
- Proven experience in building scalable data engineering frameworks and foundational services to support enterprise-wide analytics initiatives.
- Hands-on expertise in Snowflake, Spark, and AWS Glue, with knowledge of event-driven architectures and streaming data solutions.
- Strong leadership experience in managing teams and delivering complex projects in Agile environments with leading team size of more than 20 members
- Proficiency in integrating Generative AI solutions into existing platforms and workflows.
- Excellent communication and stakeholder management skills to collaborate across diverse teams.