Senior Software Engineer, Data Infrastructure

About the Role

Key Responsibilities

• Design, develop, and maintain data infrastructure capabilities that enable teams to effectively and securely produce, consume, and utilize data from internal systems and third-party platforms.
• Build a platform for deploying AI-powered and LLM-backed applications that leverage production-grade, governed data.
• Scale analytics pipelines and data models to support a rapidly growing and increasingly complex business.
• Enable data discovery, lineage, and quality monitoring to support trust and compliance in AI training and inference pipelines.
• Implement change data capture (CDC) for Postgres and Kafka to support real-time data availability.
• Adopt modern table formats (e.g., Apache Iceberg) to enable incremental processing and time travel for ML features and analytical workflows.
• Operationalize a security classification and governance framework to support responsible AI and data privacy.
• Generate synthetic datasets that replicate production schema and distributions for safe development, testing, and AI model evaluation.

Requirements

• Strong programming skills in Python and/or Go, with a track record of delivering reliable, well-tested systems.
• Deep knowledge of data engineering fundamentals, including warehousing, modeling, and transformation (e.g., dbt, SQL).
• Experience designing and building infrastructure that supports both batch and streaming data processing at scale.
• Experience building infrastructure that supports AI and ML workflows, such as feature generation, model inference, or vectorized search.
• Familiarity with data lakehouse formats (e.g., Apache Iceberg, Delta Lake) and the benefits they provide for AI-scale workloads.
• A strong understanding of data governance, security, and compliance principles, especially in regulated or privacy-sensitive domains.
• Proficiency with cloud-native data infrastructure in AWS (e.g., EKS, S3, IAM) and infrastructure-as-code (e.g., Terraform, Pulumi).
• Proven ability to collaborate with cross-functional partners in a fast-paced, security-conscious environment.

Nice to Have

• Experience enabling LLM use cases through retrieval-augmented generation (RAG), vector search, or synthetic data generation.

Qualifications

• No specific formal educational qualifications specified in the description.

Benefits & Perks

• Full medical, dental, and vision insurance + OneMedical membership
• Healthcare and Dependent Care FSA
• 401(k) with company match
• Flexible PTO
• Wellbeing + Learning & Growth reimbursements
• Paid parental leave + Fertility benefits
• Pet insurance
• Student loan refinancing
• Virtual resources for mindfulness, counseling, and fitness