Staff Software Engineer

What You’ll Be Doing

• Design, build, and maintain scalable, cloud-native data pipelines for batch and streaming workloads using modern tools like Airflow, Kafka, and dbt.
• Ensure data reliability, observability, and trust through robust monitoring, testing, and quality enforcement.
• Build data models and infrastructure that serve analytics, business operations, and AI/ML workloads.
• Partner with ML, analytics, and product teams to support the deployment of AI-powered features, from data ingestion to feature engineering and model serving.
• Develop infrastructure that enables reproducible ML workflows and data versioning, in collaboration with Data Science and ML Engineering.
• Build systems that power intelligent search, personalized recommendations, dynamic pricing, and other predictive features.
• Lead efforts to modernize our legacy data systems into modular, scalable, and cost-effective architectures using technologies like Snowflake, S3, Glue, and Redshift.
• Define and drive best practices around data governance, privacy, and access control.
• Champion data discoverability and self-service access across internal teams.
• Working closely with the VP of Engineering to provide architectural guidance across the engineering organization for high-scale data solutions.
• Mentor team members and promote engineering excellence through code reviews, knowledge sharing, and system design.
• Evaluate and introduce emerging tools, frameworks, and patterns that align with our AI and data strategy.

What We’re Looking For

• 8+ years of experience in software/data engineering, with a focus on distributed systems, data platforms, or cloud infrastructure.
• Demonstrated success leading large-scale data infrastructure initiatives and mentoring engineers.
• Experience supporting analytics and ML workflows in a production environment.
• Proficient in Python and SQL; familiarity with JVM-based languages is a plus.
• Strong understanding of streaming and batch processing frameworks (e.g., Kafka, Spark, Airflow).
• Experience with modern data stack tools (e.g., dbt, Snowflake, Redshift, S3, Glue).
• Familiarity with AI agent workflows, including orchestration of multi-step, goal-driven agents using tools or frameworks like LangChain, Semantic Kernel, or custom-built solutions, is a plus.
• Deep understanding of data architecture patterns, including event-driven systems, data lakes, and warehouse modeling.
• Proven ability to lead cross-functional initiatives with Product, Data Science, and Engineering.
• Excellent communication skills and a collaborative, pragmatic mindset.
• Comfortable working in an Agile environment with CI/CD pipelines and DevOps practices.