Customer Data Engineer (Python, Django, SQL)

Location: Remote (LATAM)

Employment Type: Full-Time

Compensation: USD $3,000–5,000/month

FitNext Exclusive: No

Company Overview

A Series A fintech company building a decision intelligence platform for independent wealth management firms and financial advisors. The platform helps advisors make better decisions through portfolio risk analytics, compliance workflows, reporting, and advisor productivity while centralizing financial data from custodians, CRMs, and market data providers into a single trusted platform.

The company is growing rapidly and is looking for engineers who enjoy solving complex customer data problems while working closely with both Engineering and Customer Success teams.

Challenge

We are looking for a Customer Data Engineer to own the investigation and resolution of production customer data issues.

Unlike a traditional backend engineering role, this position focuses on understanding why customer data is incorrect, tracing problems across databases, ETL pipelines, APIs, and application logic, then implementing durable fixes that improve the overall reliability of the platform.

You'll serve as the technical bridge between Engineering and Customer Support, helping explain complex issues to non-technical teams while building internal tooling and improving processes that reduce recurring incidents.

Key responsibilities include:

• Investigate and resolve production customer data issues from symptom to root cause.
• Reconcile and safely correct customer records within the platform.
• Build internal diagnostic and troubleshooting tools to improve investigation efficiency.
• Improve ETL processes and data workflows to prevent recurring issues.
• Analyze SQL data discrepancies and validate data consistency across systems.
• Collaborate closely with Engineering, Support, and Customer Success teams.
• Communicate technical findings and resolution status to non-technical stakeholders.
• Escalate architecture or platform-level issues when deeper engineering changes are required.
• Document root causes, solutions, and preventive improvements for recurring incidents.

Must Have

• 3+ years of professional experience in Software Engineering, Data Engineering, Technical Support Engineering, Solutions Engineering, or similar roles.
• Professional experience with Python and Django.
• Strong SQL skills, including data validation, reconciliation, and troubleshooting.
• Experience investigating and resolving production data issues.
• Strong root cause analysis and debugging skills.
• Experience working with ETL processes or data pipelines.
• Experience collaborating directly with customer-facing or support teams.

Nice to Have

• Experience in Support Engineering, Customer Success Engineering, Solutions Engineering, or Implementation Engineering.
• Experience working in SaaS environments.
• Experience in fintech, wealth management, or financial services.
• Familiarity with custodial feeds, CRM integrations, or market data providers.
• Experience building internal diagnostic or troubleshooting tools.
• Experience with monitoring and observability platforms (Datadog, Sentry, CloudWatch, etc.).
• Familiarity with financial data such as portfolios, holdings, securities, or transactions.

Soft Skills

• Strong analytical thinking and problem-solving skills.
• Excellent root cause investigation mindset.
• Customer-first attitude with a focus on delivering reliable solutions.
• Ability to communicate complex technical concepts clearly.
• Strong ownership and accountability.
• High attention to detail and data accuracy.
• Comfortable working across Engineering and Customer Success teams.
• Self-motivated and effective in a remote-first environment.
• Excellent English communication skills, including explaining technical issues to non-technical audiences.

Benefits

• Fully remote position across LATAM.
• Competitive compensation ranging from USD $3,000 to $5,000 per month.
• Opportunity to join a growing Series A fintech company.
• High ownership and direct impact on customer experience and platform reliability.
• Exposure to large-scale financial data systems and production environments.
• Small, collaborative team with close interaction across Engineering and Customer Success.
• Long-term growth opportunities within the company.