Senior Data Engineer
New
S
Saaf AIMortgage lending
Location: India; Workplace: RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years in a data engineering or similar backend data-focused role.
- Required Skills
- AWSPythonSQLSnowflakeCI/CDData modelingdbt
Requirements
- Have 5+ years in data engineering or a similar backend data-focused role.
- Demonstrate strong SQL and Python development skills for data transformation and automation.
- Have experience with modern ETL/ELT frameworks such as dbt.
- Be proficient with cloud platforms; AWS is preferred, along with serverless data services.
- Have strong experience with data warehouse technologies; Snowflake is preferred.
- Be skilled in API integrations and ingestion from third-party systems.
- Be proficient in data modeling, including Kimball/Star schema and Data Vault.
- Demonstrate regular use of AI-powered development tools such as Cursor, GitHub Copilot, or Claude Code.
- Have a proven track record of delivering production-grade data pipelines at scale.
- Have experience implementing CI/CD practices for data workflows.
- Have experience collaborating with product managers, data scientists, and full stack engineers.
- Preferred: experience building agentic workflows and orchestrating real-time multi-step automation.
- Preferred: experience with financial datasets and APIs in high-compliance environments, and understanding of GDPR and CCPA.
- Preferred: experience with prompt engineering for code generation, data transformations, or AI-powered data workflows.
Responsibilities
- Design, implement, and maintain ETL/ELT pipelines for structured and unstructured data.
- Build and optimize data warehouses and marts for analytics, reporting, and product use cases.
- Design and maintain conceptual, logical, and physical data models.
- Ingest data from APIs, SaaS platforms, and internal systems; maintain connectors and ingestion frameworks.
- Implement data validation, schema management, documentation, profiling, anomaly detection, and lineage support.
- Use AI-assisted tools for pipeline development, debugging, testing, documentation, and query optimization.
- Build and support agentic workflows and multi-step automated processes that act on data in real time.
- Monitor and tune pipeline and warehouse performance; establish logging, monitoring, and alerting.
- Apply data security and privacy controls aligned with financial regulatory requirements.
- Provide datasets for analysts, product managers, and operational teams, collaborating with product managers, data scientists, and full stack engineers.
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