Customer Data Engineer (Integrations)

Full-Time | Remote | 8am – 5pm EST We're hiring a Customer Data Engineer (Integrations) to own the correctness of customer data in mission-critical software for a highly regulated financial services market. This person will own the full path of customer data — from the integrations that feed it (custodians, CRMs, and financial data providers) through to every service that consumes it — making sure accounts, investors, portfolios, securities, goals, and transactions are accurate, deduplicated, and consistent everywhere they appear. This is a hands-on individual-contributor role with deep ownership, sitting within Success Engineering and reporting to the Success Engineering Manager. You'll be the point person whenever customer data looks wrong — bringing new integrations online, migrating customers between providers without creating duplicates, and directly reducing the churn that data-quality problems cause. What you'll own The customer data lifecycle, end to end: Integration onboarding and provider-to-provider migrations — source analysis, mapping into StratiFi's models, stable match keys, cutover plans, reconciliation sign-off, and rollback Data reconciliation and quality — defining what "correct" means, building automated checks, and catching duplicates, gaps, and misclassifications before they reach advisors or corrupt downstream analytics Cross-service consistency — monitoring propagation from the system of record to downstream, derived, and search services, and resolving drift, lag, and dropped updates before customers see stale data Data issue resolution — triaging discrepancies raised by Support, CS, and Success Engineering, tracing them to root cause across source, ingestion, mapping, storage, and sync, and fixing them durably Systems improvement — driving better ingestion methods, consistency mechanisms, and performance as accounts and data volume grow, and automating away manual, error-prone steps You'll keep customer data provably correct — and feed every recurring issue back into checks and design so it doesn't return. You're a fit if you: Build robust, automated ingestion/ETL from third-party APIs and file feeds in Python (or similar) — idempotent, restartable, and instrumented with monitoring and alerting. Write efficient SQL, work confidently in production relational databases, and understand indexing, partitioning, and data-volume trade-offs. Know financial and securities data well enough to spot when it's wrong — portfolios, holdings, transactions, custodial feeds, corporate actions, and classification nuances. Treat data quality as an engineering discipline — testable rules, automated validation, and reconciliation built into the flow rather than eyeballed. Design mapping logic that normalizes messy external sources into clean, consistent models, with stable identifiers that prevent duplicates. Understand backend systems deeply enough to reason about eventual consistency, propagation, and where data can drift between services. Debug like a detective — forming and testing hypotheses, distinguishing source errors from pipeline, mapping, or sync bugs, and documenting findings. Explain data issues clearly to non-technical stakeholders and deliver predictably within Scrum/Kanban. Strong plus: experience with non-relational and search/index stores, financial services or wealth management domain knowledge, and working from requirements in Confluence or Notion. Benefits 100% remote Competitive salary in USD International team and experience