Senior Software Engineer

About the Role

We are seeking a Senior Software Engineer with extensive expertise in data engineering to craft scalable data pipelines, deliver data products, and establish robust analytics infrastructure. This role requires a candidate who is adept at translating business needs into practical solutions, balancing rapid prototyping with production-level data engineering efforts, and who thrives in a collaborative environment that values strong ownership.

Key Responsibilities

• Develop and maintain scalable data pipelines and analytics foundations that support business intelligence, AI/ML, and advanced analytics.
• Design efficient data models, performant SQL queries, and transformation logic tailored to business requirements.
• Utilize Python and PySpark for processing large-scale data and automating workflows.
• Engage with business stakeholders to deeply understand requirements and deliver actionable data solutions.
• Transition smoothly between experimental prototyping and production-grade implementations based on the demands of the business.
• Ensure data products are well governed, trusted, and facilitate visualization, BI, and AI/ML workflows.
• Collaborate across teams with a strong sense of ownership to achieve shared objectives.

Required Qualifications and Skills

• MS or BS degree in Computer Science or a related science or engineering field.
• Over six years of hands-on experience in data engineering, analytics engineering, or delivering data platforms.
• Advanced proficiency in SQL for creating optimized queries and data modeling.
• Strong command of Python and PySpark for large-scale data processing tasks.
• Experience designing and operating ETL/ELT pipelines, workflow orchestration, data quality assurance, and production-grade data products.
• Familiarity with cloud-based data platforms, distributed data processing patterns, and analytics/BI consumption models.
• Knowledge or exposure to AI/ML engineering practices such as feature pipeline development, model productionization, leveraging large language models (LLMs), and agentic AI patterns is a plus.
• Understanding of DevOps and DataOps methodologies, including continuous integration/continuous deployment (CI/CD), monitoring, observability, and incident management.