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Senior Data Engineer
Saaf Finance. Design, implement, and maintain ETL/ELT pipelines for structured and unstructured datasets from internal and external sources .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and maintaining ETL/ELT pipelines, data modeling, and cloud platforms, with a strong focus on leveraging AI tools for data transformation and automation. Proven ability to collaborate with cross-functional teams to deliver scalable data solutions in compliance with financial regulations.
Highest-signal resume keywords
ETL/ELT Pipeline DevelopmentSQL and Python DevelopmentData Warehouse Technologies (Snowflake)AI-Powered Development ToolsData Modeling (Kimball/Star Schema)
ATS Keywords
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Hard Skills
ETL/ELT Pipeline DevelopmentSQLPythonData Warehouse TechnologiesData ModelingAPI IntegrationsCI/CD PracticesData TransformationData ProfilingAnomaly Detection
Soft Skills
CollaborationResourcefulnessAdaptability
Tools & Technologies
AWSDbtCursorGitHub CopilotClaude CodeSnowflake
Industry Keywords
Data EngineeringFinancial Regulatory ComplianceData Privacy Regulations (GDPR, CCPA)Agentic WorkflowsHigh-Compliance Environment
Tech Stack
Tools & technologiesAWSCloudETLPythonSQLVault
About the role
Key responsibilities & impact- 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
Requirements
What you’ll need- 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
Benefits
Comp & perks- Competitive salary
- Unlimited PTO
- Remote-first with flexible hours
- Upto $2,000/year professional development budget
- Home office setup stipend