Overview In this role, you help design and build scalable data platforms and pipelines to power insights. You'll partner with analytics, product, finance, and operations to translate business needs into reliable data solutions. You will apply software engineering best practices to data workflows, focusing on quality, performance, and governance. You'll work on architectures for data warehousing, streaming, and ML data pipelines, contributing to a data-driven, privacy-conscious environment. This is an opportunity to shape data products that enable decision-making across our ecosystem.
Responsibilities- Design, build, and optimize ETL/ELT data pipelines using Python, SQL, and orchestration tools
- Develop and maintain data models, APIs, and microservices for analytical and operational use cases
- Collaborate with cross-functional partners to translate business needs into engineering solutions
- Apply software engineering best practices (version control, CI/CD, testing, observability) to data workflows
- Optimize data quality, scalability, and latency across distributed systems (Snowflake, Spark, Databricks)
- Participate in architecture discussions on data warehousing, event streaming, and ML data pipelines
- Ensure compliance with privacy, security, and governance standards in data operations
Key requirements- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 3-7 years of experience in software or data engineering
- Advanced proficiency in Python (Pandas, PySpark, or similar)
- Strong SQL expertise with complex queries and stored procedures
- Experience with data modeling, schema design, and performance tuning
- Experience building or orchestrating workflows using Airflow, Dagster, or similar tools
- Solid understanding of APIs, CI/CD pipelines, Git, and containerization (Docker/Kubernetes)
- Cross-functional collaboration
- Problem-solving
- Strong communication
- Python
- SQL
- Pandas
