Capgemini
Insurance
AnalyticsEngineer,ServiceOpsAnalytics&AI
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Analytics Engineer, Service Ops Analytics & AI at Capgemini. Skills: Data pipelines, Analytics solutions, Data products, AI-driven solutions. Lead data pipeline design. Lead data pipeline development”
What You'll Achieve.
Enable business intelligence; Enable data science; Enable advanced analytics; Fuel business growth; Enhance customer experience
Industry & Context.
Problem-solving skills; Analytical skills
What They're Looking For.
Must Have
Hands-on experience with SQL, Hands-on experience with Python, Hands-on experience with dbt, Hands-on experience with Snowflake, Experience in version control systems, Experience with workflow management tools, Proven experience in designing data pipelines, Proven experience in building data pipelines, Proven experience in designing architectures, Proven experience in building architectures, Understanding of data governance, Understanding of data quality assurance, Understanding of performance optimization, Expertise in ETL/ELT processes, Expertise in data modeling, Expertise in integration of data, Experience with CI/CD workflows, Experience with CI/CD tools, Problem-solving skills, Analytical skills
Nice to Have
Experience with Git, Experience with Airflow
What You'll Do.
Lead data pipeline design
Lead data pipeline development
Lead data pipeline deployment
Establish best practices for data engineering
Uphold best practices for data engineering
Participate in code reviews
Provide constructive feedback
Contribute to team's continuous improvement
Design ETL/ELT pipelines
Build ETL/ELT pipelines
Maintain ETL/ELT pipelines
Design reusable frameworks
Build reusable frameworks
Maintain reusable frameworks
Design reusable libraries
Build reusable libraries
Maintain reusable libraries
Proactively monitor data pipelines
Troubleshoot data pipelines
Implement CI/CD pipelines
Streamline deployment
Streamline maintenance
Partner with data scientists
Partner with engineers
Partner with analysts
Partner with product managers
Partner with business stakeholders
Understand requirements
Translate requirements into technical specifications
Deliver impactful data solutions
Articulate complex technical concepts
Ensure shared understanding
How You'll Work.
Team & Collaboration
Cross-functional collaboration; Partner with stakeholders
Communication Scope
Stakeholder communication; Articulate technical concepts
Full Job Description
The goal of analytics engineering team within the Service Analytics and AI organization is to build curated data products leveraging data from structured and unstructured enterprise data sources to enable business intelligence, data science, and advanced analytics. Seeking a highly skilled and motivated data engineer to join Analytics Engineering team within the Service Analytics and AI organization. This role is pivotal in designing, building, and maintaining scalable data pipelines and analytics solutions that empower Advanced Analytics, Business Intelligence, and Data Science initiatives. You will play a crucial role in building a semantic data layer, defining and implementing cutting-edge data products, and delivering innovative AI-driven solutions that fuel business growth and enhance customer experience. **Requirements** **Key Responsibilities** * **Data Pipeline Development:** Lead the design, development, and deployment of scalable and robust data pipelines, ensuring seamless data integration and processing across diverse systems. * **Analytics** **Engineering Best Practices:** Establish and uphold best practices for data engineering, including coding standards, data governance, performance optimization, and automation strategies. * **Code Quality and Review:** Participate in code reviews, provide constructive feedback, and contribute to the team's continuous improvement in coding practices and methodologies. * **ETL/ELT Development:** Design, build, and maintain robust ETL/ELT pipelines, reusable frameworks, and libraries to process and transform data from diverse sources, ensuring accuracy, quality, and consistency. * **System Monitoring:** Proactively monitor and troubleshoot data pipelines, ensuring high availability, reliability, and performance across all data engineering workflows. * **Automation and CI/CD:** Implement CI/CD pipelines to streamline the deployment, testing, and maintenance of analytics engineering processes. * **Cross-functional Collab
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