Senior Data Engineer

• Design, implement, and maintain ETL/ELT pipelines for structured and unstructured datasets from internal and external sources
• Leverage AI-assisted development tools to accelerate pipeline authoring, generate transformation logic, and automate boilerplate code
• Build and optimize data warehouses and marts for analytics, reporting, and product use cases
• Design, implement, and maintain conceptual, logical, and physical data models
• Ingest data from APIs, SaaS platforms, and internal systems into the core data platform
• Build and maintain reliable connectors and ingestion frameworks handling schema evolution, rate limits, and error recovery
• Implement validation, schema management, and documentation for data accuracy and compliance
• Use AI tools for data profiling, anomaly detection, and automated documentation of lineage and transformations
• Evaluate and integrate emerging AI tools into the team's data development process
• Build and support agentic workflows and multi-step automated processes acting on real-time data
• Apply AI-assisted analysis to debugging pipeline failures, optimizing query performance, and identifying data quality issues
• Monitor and fine-tune pipeline and warehouse performance for scalability and cost efficiency
• Set up logging, monitoring, and alerting for data jobs
• Apply data security and privacy controls aligned with financial regulatory requirements
• Provide clean, consistent datasets for analysts, product managers, and operational teams
• Collaborate with product managers, data scientists, and full stack engineers to align data models with business needs

• 5+ years in a data engineering or similar backend data-focused role
• Strong SQL and Python development skills for data transformation and automation
• Experience with modern ETL/ELT frameworks such as dbt
• Proficiency with cloud platforms (AWS preferred) and serverless data services
• Strong experience with data warehouse technologies (Snowflake preferred)
• Skilled in API integrations and ingestion from third-party systems
• Proficient in data modeling (Kimball/Star schema, Data Vault)
• Demonstrated, regular use of AI-powered development tools (e.g., Cursor, GitHub Copilot, Claude Code, or similar) to accelerate data pipeline development, debugging, or documentation
• Proven track record of delivering production-grade data pipelines at scale
• Experience implementing CI/CD practices for data workflows
• Experience collaborating closely with product managers, data scientists, and full stack engineers
• Startup mindset: hands-on, resourceful, and comfortable operating in a fast-paced environment
• Preferred: Experience building agentic workflows and orchestrating multi-step automated processes that act on data in real time
• Preferred: Familiarity with data engineering patterns and infrastructure required for AI-powered tools and automation platforms
• Preferred: Experience working with financial datasets and APIs in a high-compliance environment
• Preferred: Understanding of data privacy regulations such as GDPR and CCPA
• Preferred: Experience with prompt engineering for code generation, data transformation logic, or building AI-powered data workflows

• Competitive salary
• Unlimited PTO
• Remote-first with flexible hours
• Upto $2,000/year professional development budget
• Home office setup stipend