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.

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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 a dashboard three days later.

Full Domain Delivery

- Deliver the full pipeline for your domain: ingestion config, raw landing, dbt staging and mart models, data-quality tests, orchestration DAG, and runbook.

- Stakeholders own the business definitions; you own translating them into correct, tested transformations — and you own the documentation that makes those definitions authoritative, traceable, and fuels our AI-ready environment.

Architecting an AI-Ready Single Source of Truth

- Design domain data so it's clean, consistently grained, well-documented, and semantically unambiguous — the kind of SSOT that both dashboards and AI/agent-driven consumers can query reliably, without hidden business rules or hallucination.

- Treat documentation and metric traceability as part of the deliverable, not an afterthought.

Platform Reliability & Performance

- Implement monitoring, alerting, and observability across your pipelines and their platform dependencies.

- Ensure data freshness and system uptime meet defined service expectations; optimize pipeline performance, compute utilization, and system efficiency.

Engineering Standards

- Maintain version-controlled data infrastructure and CI/CD workflows for your pipelines.

- Set the reference implementation others follow — especially for ingestion and DQ, where the team currently lacks a pattern.

Mentorship & Collaboration

- Review other engineers' work, pair on hard problems, and