Senior Data Engineer

Our Story TopDog Law is not your typical law firm. We're a nationally scaling personal injury firm built for impact and growth — owning the client experience end-to-end, from marketing and intake through litigation. We believe that world-class marketing, paired with exceptional legal talent and operations, creates better experiences and outcomes for clients and the business alike. Over the past three years, we've grown 2–3x year over year, setting a new standard on the marketing side of the personal injury space and proving what's possible when strategy, speed, and execution align. Now we're applying that same discipline and innovation to firm operations, case management, and national scale — intentionally building the infrastructure, systems, and teams to grow without sacrificing quality, culture, or accountability. We are a fully remote team that share trust, open communication, and a commitment to doing great work. If you love ownership, thrive in a fast-moving environment, and want to help build something exceptional, you'll feel right at home here. The Opportunity We're hiring a Senior Data Engineer to own a business domain's data end-to-end, with a specific mandate: be the person who makes ingestion reliable and the numbers trustworthy. You are expected to be the trusted authority on getting data in correctly and proving it's right for your division. You'll partner closely with the Director of Data to turn platform strategy into reliable production systems, and own the day-to-day reliability, performance, and scalability of what you build. What You Will Own Ingestion, End-to-End Design and build how source data lands — third-party connectors, APIs, webhooks, file drops, CDC and batch loads. Master API-based ingestion : authentication and token-refresh flows (OAuth2, API keys), pagination, rate-limit handling, retry and backoff, and reconciling incremental/paginated pulls into complete, correct datasets. Own schema-drift handling, incremental vs. full-refresh strategy, idempotency and replayability, backfills, and late/duplicate-record handling. Data Quality as a First-Class Deliverable Build the tests, contracts, and monitoring that let stakeholders trust the numbers: freshness and volume checks, schema/type enforcement at the boundary, referential and uniqueness constraints, reconciliation against source-of-truth, and anomaly detection on business-critical metrics. Ensure failures surface loudly and early — caught at the boundary, not discovered in...

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