Analytics Engineer, Service Ops Analytics & AI
At a glance
Mid-level Data Analysis & BI role at Capgemini. New York · full-time · no salary stated · we estimate $97,500–$149,205.
Capgemini has not published a salary for this role. The figure below is our estimate, not theirs.
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain pipelines that bring together data from varied systems for analytics use
- Create reusable ETL/ELT frameworks that transform source data while protecting accuracy and consistency
- Set engineering practices for governance, code quality, performance, and automation
- Monitor data workflows, investigate failures, and improve their reliability and speed
- Turn partner needs into technical plans, review code, and explain data work to nontechnical teams
What you bring
- Hands-on ability with SQL, Python, dbt, and Snowflake
- Experience using Git and Airflow or comparable tools in those categories
- A track record designing scalable data pipelines and architectures
- Working knowledge of data modeling, governance, quality checks, performance tuning, and ETL/ELT
Who this fits
This mid-level, full-time role suits someone who builds data pipelines and analytics solutions in close collaboration with technical and business partners. The position is onsite in New York.
From the employer
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 Collaboration: Partner with data scientists, engineers, analysts, product managers, and business stakeholders to understand requirements, translate them into actionable technical specifications, and deliver impactful data solutions.
- Stakeholder Communication: Articulate complex technical concepts to non-technical stakeholders, fostering alignment and ensuring a shared understanding of data initiatives across teams.
Qualifications
- Hands-on experience with SQL, Python, dbt, and Snowflake.
- Experience in version control systems such as Git, and workflow management tools such as Airflow
- Proven experience in designing and building scalable data pipelines, and architectures.
- Strong understanding of data governance, quality assurance, and performance optimization in a data engineering context.
- Expertise in ETL/ELT processes, data modeling, and integration of data from multiple sources into a data warehouse.
- Experience with CI/CD workflows and tools for data engineering.
- Strong problem-solving and analytical skills, with the ability to work effectively in a collaborative environment.
If you are passionate about data engineering and ready to take on a challenging, impactful role, we encourage you to apply. Join us in building the next-gen data ecosystem that powers the future of Insurance Service and Customer Analytics!
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