Job Summary

Are you a Software Engineer looking for an cutting-edge role

Do you enjoy being part of a team that works with a diverse range of technology

About our Team

Elsevier Health applies innovation, facilitates insights, and helps drive more informed decision-making for our customers across global health. We support health providers by providing accessible, trusted evidence-based information; prepare more medical and nursing students with effective tools and resources; provide insights that help clinicians improve patient outcomes; and supports a more personalized and localized healthcare experience. All for the benefit of every patient.

This role sits within the Sales Marketing Data Analytics team, part of the wider Digital Business Solutions (DBS) group. Our team provides the analytical foundations that drive two of Elseviers most revenue-critical functions: sales performance and marketing effectiveness.

On the Sales side, accurate and timely reporting is critical to incentivising and guiding our global sales force. Opportunities data warehouse provides analytics on salesperson pipeline, actual sales, and quota, and Agreements data warehouse provides analytics on sales agreements and renewals.

On the Marketing side, the Contacts data warehouse enables global marketing teams to design effective marketing campaigns with the desired segmentation for the business area and product they want to focus.

There are also some standalone datasets ingested from multiple sources, some are quite huge serving analytics needs for various data and analytical teams across the business.

You will be working on cutting-edge technology and helping with the latest companywide AI initiatives such as Spec-driven development within the Sales and Marketing squad.

About the Role

As a Senior Software Engineer at Elsevier,



you will be responsible for the technical design, delivery, and evolution of the Sales Marketing Data Analytics platform. The role owns the design of scalable data solutions, drives engineering best practices, mentors engineers, and partners with Product, Analytics, and business stakeholders to deliver reliable, secure, and high-quality data products. The engineer is expected to work independently, influence technical direction across the squad, and lead complex initiatives from discovery through production.

Responsibilities

- Lead architecture and technical design for data platforms, pipelines, and warehouse solutions.
- Own delivery of complex initiatives from design through production support.
- Design, build, and optimise scalable Snowflake and DBT data pipelines.
- Define data models, performance standards, observability, and reliability practices.
- Lead technical design reviews and establish engineering standards.
- Champion code quality through reviews, automated testing, CI/CD, and DevSecOps practices.
- Mentor engineers through pair programming, coaching, and technical guidance.
- Partner with Product Owners, Analysts, and stakeholders to shape technical roadmaps.
- Drive continuous improvement by reducing technical debt and improving platform performance.
- Lead incident analysis, root-cause investigations, and reliability improvements.




- Evaluate and introduce emerging technologies including AI-assisted engineering and specification-driven development where they provide measurable value.

Requirements

What Were Looking For

Beyond technical skills, we value engineers who bring:

- Strong problem-solving ability - comfortable with ambiguous requirements and large, complex datasets
- Clear communication - able to explain technical concepts to both technical and non-technical stakeholders
- Collaborative mindset - builds relationships across the business and works well in cross-functional teams
- Technical ownership - takes pride in code quality, reliability, and continuous improvement
- Curiosity and growth mindset - enthusiastic about learning, including emerging technologies such as AI and Spec-driven development

Experience Skills

Essential:

- Significant professional experience designing and operating enterprise data warehouse solutions.
- Expert knowledge of Snowflake, DBT, SQL, and dimensional data modelling.
- Strong experience with AWS-based data platforms.
- Experience building production-grade CI/CD pipelines using GitHub and Jenkins or equivalent.
- Strong Python development skills for data engineering.
- Experience leading technical delivery and mentoring engineers.
- Excellent stakeholder communication and ability to influence technical decisions.
- Experience applying AI-assisted engineering or LLM-enabled developer tooling.

Desirable:

- Working knowledge of AWS cloud services and Apache Airflow
- Experience with infrastructure-as-code, particularly Terraform
- Programming experience in Python and/or Java

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr Software Engineer I (Chennai)
🏢 Elsevier
📍 Chennai