Senior Data Engineer

TopDog Law is a nationally scaling personal injury firm built for impact and growth, with operations spanning marketing, intake, litigation, and firm operations. The Senior Data Engineer will own a business domain’s data end-to-end, building reliable ingestion, data-quality systems, production pipelines, and AI-ready single sources of truth while partnering with stakeholders and mentoring engineers.

Responsibilities:

• 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
• 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
• 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
• 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
• 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
• 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
• Review other engineers' work, pair on hard problems, and raise the team's bar — senior impact shows up through others, not only your own commits
• Translate business needs into data solutions directly with your domain's stakeholders; understand not just how to build it but why the business needs it built that way

Requirements:

• 5+ years building and maintaining production data pipelines
• Expert-level dbt — staging/mart architecture, incremental models, tests, macros, documentation, and exposures. This is non-negotiable
• Mastery of API-based data ingestion — authentication and token-refresh (OAuth2, keys), pagination, rate limiting, retry/backoff, and assembling incremental pulls into complete, correct datasets
• Demonstrated expertise across other ingestion source types (webhooks, files, databases/CDC), including schema drift, idempotency, backfills, and incremental loading
• A strong track record designing data-quality frameworks — not a handful of not_null tests, but freshness/volume/reconciliation/anomaly checks and the alerting around them
• Strong Python for custom extraction, loading, and tooling — able to build and maintain production ingestion code, not just glue scripts
• Fluent SQL and hands-on experience with a modern cloud data warehouse (BigQuery or Snowflake preferred; comparable considered )
• Experience architecting a clean, documented, AI-ready single source of truth that downstream analytics and AI consumers can trust without hidden logic
• Experience with orchestration (Airflow, Dagster, or dbt Cloud jobs) and with Git in collaborative development environments