Senior Data Engineer
New
J
JobgetherFinancial technology
Based in IndiaFull-TimeSenior
SalaryUp to $2,000 per year for professional development; home office setup stipend
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Job Details
- Experience
- At least 5 years of experience in data engineering or a related backend, data-focused engineering role
- Required Skills
- AWSPythonSQLSnowflakeCI/CDData modelingdbt
Requirements
- Have at least 5 years of experience in data engineering or a related backend, data-focused engineering role.
- Have a proven track record of delivering production-grade data pipelines at scale.
- Bring strong SQL and Python skills for transformation logic, workflow automation, and troubleshooting.
- Have hands-on experience with modern data engineering frameworks and tools such as dbt.
- Have cloud platform experience, preferably AWS, including serverless data services, and familiarity with modern data warehouses, ideally Snowflake.
- Have experience with data modeling approaches such as Kimball dimensional modeling, Star Schema, or Data Vault.
- Demonstrate experience integrating third-party APIs and external data sources, including managing schema evolution, rate limits, and errors.
- Regularly use AI-assisted engineering tools such as Cursor, GitHub Copilot, or Claude Code, or similar solutions.
- Have experience operating production data systems and implementing CI/CD, monitoring, testing, and performance optimization.
- Work effectively with product managers, data scientists, analysts, and full-stack engineers to translate business requirements into data solutions.
- Bring a hands-on, resourceful, adaptable approach and the ability to work independently and manage changing priorities.
- Familiarity with agentic workflows and real-time automated processes is an advantage; financial data, regulated environments, GDPR or CCPA, and prompt engineering experience are beneficial.
Responsibilities
- Build, maintain, and optimize scalable ETL/ELT pipelines for structured and unstructured data.
- Develop data warehouses, data marts, and conceptual, logical, and physical data models.
- Create ingestion frameworks and API connectors for CRM platforms, financial data services, SaaS applications, and internal systems.
- Establish validation rules, schema management, documentation, and data quality controls.
- Use and evaluate AI-powered coding assistants, code generation, and automated testing tools.
- Build and support multi-step automated workflows for real-time validation, enrichment, and other data operations.
- Monitor and tune pipelines, warehouse queries, and processing jobs; implement logging, monitoring, and alerting.
- Apply data privacy, security, and governance controls appropriate for financial services.
- Deliver structured datasets for analysts, product managers, and operational teams, collaborating with data scientists and full-stack engineers.
- Contribute to production development standards, CI/CD workflows, automated testing, documentation, and continuous improvement.
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