Posted Jul 8, 2026
Remote (Latin America-based)
Competitive compensation in USD
Full-time, contractor. Work hours should overlap with Eastern to Central time zones.
Tech Stack: Python, Pandas, SQL, APIs, AWS, ETL/data pipelines
At Near, we connect top talent in Latin America with exciting remote opportunities at U.S.-based companies. Our mission is to create better lives by fostering a remote work culture that transcends borders.
Our client is a growing fintech company in the wealth-tech space, helping financial advisors and RIAs through a platform focused on risk analytics, compliance, customer data accuracy, and operational efficiency. They are scaling their product and team as they prepare for new enterprise growth and increased product complexity.
We’re looking for a Customer Data Engineer who combines strong data engineering fundamentals with sharp investigative instincts. You’ll own the correctness of customer data flowing through the platform, from integrations and migrations through reconciliation, downstream consistency, and issue resolution. You’ll collaborate closely with Success Engineering and backend stakeholders to improve reliability, reduce customer-facing data issues, and make onboarding and migrations faster and more repeatable.
Own onboarding of new integrations and provider-to-provider migrations without creating duplicate records.
Reconcile customer data such as accounts, investors/prospects, model portfolios, securities, goals, and transactions against source systems.
Build and improve automated checks, validation logic, and reconciliation workflows to catch issues early.
Monitor data consistency across source systems, downstream services, and derived systems.
Investigate and resolve customer data discrepancies raised by Support, CS, and Success Engineering.
Improve ingestion, sync, and storage approaches to raise performance, consistency, and maintainability.
Work with APIs, ETL pipelines, Python workflows, SQL databases, and AWS-based infrastructure.
Senior-level data engineering experience.
Strong experience with Python, Pandas, SQL, ETL, and API integrations.
Experience working with relational databases and data-heavy systems.
Ability to build idempotent, reliable ingestion and reconciliation workflows.
Strong debugging and root-cause analysis skills across ingestion, mapping, storage, and sync layers.
Comfortable working within an existing data infrastructure rather than building the function from scratch.
Strong English communication skills; this role is not primarily client-facing, so English is important but not the deciding factor.
Experience in a high-growth company that has scaled from a more unstructured environment into a more structured team setup is strongly preferred across all engineering hires. Akhil explicitly wanted candidates from software product companies rather than consulting/dev shops.
Financial or securities data experience. Akhil said this is helpful but not required.
Exposure to customer data migrations, provider transitions, or reconciliation-heavy environments.
Familiarity with AWS services, including CloudWatch and time-series or related data tooling.
Experience supporting customer success or post-sales technical workflows.
Competitive compensation in USD.
Benefits: U.S. holidays and unlimited PTO.