Lead Data Engineer

About Chubb

Chubb is a world leader in insurance. With operations in 54 countries and territories, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance and life insurance to a diverse group of clients. The company is defined by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength and local operations globally. Parent company Chubb Limited is listed on the New York Stock Exchange (NYSE: CB) and is a component of the S&P 500 index. Chubb employs approximately 40,000 people worldwide. Additional information can be found at: www.chubb.com.

About Chubb India

At Chubb India, we are on an exciting journey of digital transformation driven by a commitment to engineering excellence and analytics. We are proud to share that we have been officially certified as a Great Place to Work for the third consecutive year, a reflection of the culture at Chubb where we believe in fostering an environment where everyone can thrive, innovate, and grow

With a team of over 2500 talented professionals, we encourage a start-up mindset that promotes collaboration, diverse perspectives, and a solution-driven attitude. We are dedicated to building expertise in engineering, analytics, and automation, empowering our teams to excel in a dynamic digital landscape.

We offer an environment where you will be part of an organization that is dedicated to solving real-world challenges in the insurance industry. Together, we will work to shape the future through innovation and continuous learning.

Position Details

Job Title: Lead Data Engineer

Function/Department: Technology

Employment Type: Full-time

Location: Hyderabad/Bangalore

Position Summary:

We are in search of a highly skilled Lead Data Engineer to join our Data Engineering team, In this pivotal role, you will drive the design and implementation of scalable data engineering frameworks, patterns, and best practices that support business objectives and enhance data utilization across the enterprise. Your expertise will be essential in ensuring that our data foundation is robust, scalable, and aligned with industry best practices.

In this role, you will:

Lead the design and development of comprehensive data engineering frameworks and patterns.Establish engineering design standards and guidelines for the creation, usage, and maintenance of data across Chubb LifeDrive innovation and build highly scalable real-time data pipelines and data platforms to support the business needs.Act as mentor and lead for the data engineering organization that is business-focused, proactive, and resilient.Implement strategies to monitor the effectiveness of data management.Serve as an engineering leader, coach data engineers and actively contribute to the data leadership team.Evaluate emerging data technologies and determine their business benefits and impact on the future-state data platform.Develop and promote a strong data management framework, emphasizing data quality, governance, and compliance with regulatory requirementsCollaborate with Data Modelers to create data models (conceptual, logical, and physical) Design and implement meta-data management processes to ensure data lineage, data definitions, and ownership are well-documented and understoodCollaborate closely with business leaders, IT teams, and external partners to understand data requirements and ensure alignment with strategic goalsAct as a primary point of contact for data engineering discussions and inquiries from various stakeholdersLead the implementation of data architectures on cloud platforms (Azure) to improve efficiency and scalabilityBuild and Implement the CI/CD and DevOps practices for data pipelines (DataOps)

Qualifications

Bachelor's degree in computer science, Information Systems, Data Engineering, or a related field; Master's degree preferredMinimum of 12 years experience in data engineering roles, with a significant focus in Life insurance domains preferred.Proven track record of successful implementation of data architecture within large-scale transformation programs or projectsProficiency in programming languages including Python, Scala, and SQL, with hands-on experience in building and optimising data pipelinesExperience with DevOps and DataOps practices including CI/CD pipeline automation, version control (Git)Comprehensive knowledge of data modelling techniques and methodologies, including data normalization and denormalization practicesHands on expertise across a wide variety of database (Azure SQL, MongoDB, Cosmos), data transformation (Databricks), change data capture and data streaming (Apache Kafka, Apache Flink) technologiesProven Expertise with data warehousing concepts, ETL processes, and data integration tools (e.g., Databricks)Experience with cloud-based data architectures and platforms (e.g., Snowflake, Azure SQL Database, Databricks)Expertise in .