Data Engineering Lead

Seer Interactive is a digital marketing consultancy that harnesses compassion, data, technology, and AI to make a mark on our communities. They are seeking a Data Engineering Lead who will set the architectural direction for the data platform, mentor engineers, and ensure the integration of AI in delivering strategic outcomes.

Responsibilities:

• Set the technical direction for Seer’s data platform — define the architecture, the standards, and the roadmap that other engineers build against
• Architect Seer’s data platform end-to-end across BigQuery, Dataflow, Airflow, Cloud Run, Cloud Functions, and Cloud Storage, making the cost, latency, and freshness tradeoffs that let us scale without surprises
• Set the standard for how we model and serve data, from dbt project structure to LookML, Looker explores, and embedded Looker experiences inside client portals and internal tools
• Mentor data engineers and analysts across the team — pair on tough problems, lead architectural reviews, leave PR comments that make people better (not just their code), and run the occasional workshop on TDD, git internals, or LLM-augmented engineering
• Champion engineering practice — TDD, modular Pytest suites, clean Git workflows, and CI/CD pipelines with multi-stage deploys. Make the bar visible, then help everyone meet it
• Tune what’s expensive: hunt down the slow query, the redundant DAG, the leaky storage policy. Cut BigQuery costs and dashboard load times, and bring the team along with you
• Leverage tools like ChatGPT, Claude, Gemini, and prompt-evaluation harnesses to accelerate research, code review, and pipeline development — and design AI-driven workflows that scale Seer’s engineering throughput
• Document and teach. Write playbooks that survive system evolution, decision records that explain why not just what, and onboarding paths that compound across new hires

Requirements:

• A track record of leading through technical authority and mentorship — setting the bar on a data engineering team, raising the level of engineers around you, and earning trust without needing a reporting line to back it up
• Expert-level fluency across the GCP data stack — BigQuery (partitioning, clustering, materialized views, cost optimization), Dataflow, Airflow, Cloud Run, Cloud Functions, Datastore, and Cloud Storage — with a track record of architecting production pipelines end-to-end
• Expert command of the analytics and BI layer — modular dbt projects, reusable LookML, Looker explores and embeds, Looker Studio dashboards that blend CRM and ad data into full-funnel attribution. You write elegant SQL and rewrite tangled SQL even better
• Expert Python — idiomatic, performant, well-tested. You build modular Pytest suites with custom plugins, treat TDD as default, and have introduced contract or snapshot testing to catch what unit tests miss
• Expert Git and version control used to unblock teammates. You've designed CI/CD pipelines with multi-stage deploys, automated test gates, and production debug discipline
• Comfort working with AI and LLM tools beyond surface-level usage — crafting layered, context-aware prompts, building evaluation harnesses to compare phrasing and sampling strategies, and integrating LLMs into engineering workflows where they earn their keep
• Strong project management discipline — comfort with Agile ceremonies that earn their keep, fluency in Wrike (or equivalent) for tracking dependencies and prioritization, and the judgment to change the format when it stops serving the team
• Communication range — you can brief an SLT stakeholder on a cost-saving initiative and a junior engineer on a tricky rebase in the same afternoon, and both walk away clearer