AHL - Saaf AI - Senior Data Engineer
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Requirements
• 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 • Preferred • Experience building agentic workflows and orchestrating multi-step automated processes that act on data in real time. • Familiarity with data engineering patterns and infrastructure required for AI-powered tools and automation platforms. • Experience working with financial datasets and APIs in a high-compliance environment. • Understanding of data privacy regulations such as GDPR and CCPA. • Experience with prompt engineering for code generation, data transformation logic, or building AI-powered data workflows.
Responsibilities
• Data Pipeline Development • 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. • Data Warehousing & Modeling • Build and optimize data warehouses and marts (Snowflake, BigQuery, or similar) for analytics, reporting, and product use cases. • Design, implement, and maintain conceptual, logical, and physical data models to ensure scalable, consistent, and high-quality datasets for downstream analytics and applications. • Integration & Ingestion • Ingest data from APIs, SaaS platforms (CRM, financial data APIs), and internal systems into the core data platform. • Build and maintain reliable connectors and ingestion frameworks that handle schema evolution, rate limits, and error recovery. • Data Quality & Governance • Implement validation, schema management, and robust documentation to ensure data accuracy and compliance. • Use AI tools to support data profiling, anomaly detection, and automated documentation of data lineage and transformations. • AI-Integrated Data Engineering • Use AI-assisted tools (code generation, intelligent autocomplete, automated testing) as a regular part of your data engineering workflow. • Evaluate and integrate emerging AI tools and practices into the team's data development process. • Build and support agentic workflows and multi-step automated processes that act on data in real time, including AI-powered data validation and enrichment. • Performance & Reliability • Monitor and fine-tune pipeline and warehouse performance for scalability and cost efficiency. • Set up logging, monitoring, and alerting for data jobs to ensure reliability and fast incident response. • Security & Compliance • Foster a security-first mindset across all data operations. • Analytics Enablement • Provide clean, consistent datasets for analysts, product managers, and operational teams to support fast, data-driven decisions. • Collaborate closely with product managers, data scientists, and full stack engineers to align data models with business needs.
Benefits
• High ownership from day one — your work will directly shape core systems and products • Fast-paced environment with quick decision cycles and minimal bureaucracy • Remote-first team with flexibility on work hours and location • Direct access to founders and cross-functional teams — no layers, no silos • Clear expectations, regular feedback, and support for professional growth • Work on real problems in a complex, high-impact industry
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