Anaplan
Senior Data Engineer
- Location
- Gurugram, India
- Last checked
- Oct 26, 2026
About this role
At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market.
What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture.
Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform.
Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins – big and small.
Supported by operating principles of being strategy-led, values-based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together!
We are seeking an experienced Senior Data Engineer to join our growing data team. The ideal candidate will have a strong background in building and maintaining scalable data pipelines and will be proficient in a variety of data technologies. You will be responsible for designing, implementing, and managing our data infrastructure to support our data analytics and business intelligence initiatives.
Key Responsibilities
- Data Pipeline Development: Design, build, and maintain robust and scalable ETL/ELT pipelines using tools like Informatica, Airflow, and Dataproc.
- Data Warehousing: Develop and manage our data warehouse on Google BigQuery, ensuring data is accurate, available, and easily accessible.
- Data Processing: Utilize Databricks and Python to process and transform large volumes of data efficiently.
- DevOps & Automation: Implement and manage CI/CD pipelines for data workflows using DevOps best practices and tools like Dataform and GitHub.
- Collaboration: Work closely with data scientists, analysts, and business stakeholders to understand their data needs and provide them with high-quality data.