Data Engineering Lead

Seer Interactive; Sheffield; On-site; GBP 110,000 - 160,000; Full time

Job summary

Seer Interactive is seeking a senior data platform architect to set the technical direction of our data stack and drive end-to-end ownership across BigQuery, Dataflow, Airflow, Cloud Run, and ingestion tools. You will shape data models, governance, and the Looker semantic layer to empower embedded analytics.

You will mentor data engineers and analysts, partner with product managers, and advocate for scalable, cost-aware solutions, balancing speed and reliability to support client outcomes.

Qualifications

• Experience designing production data pipelines end-to-end.
• Strong knowledge of the GCP data stack and Looker.
• Proven leadership and mentoring in data teams.
• Ability to estimate deadlines and communicate status clearly.
• Experience reducing costs and improving performance.

Responsibilities

• Define architectural standards for Seer’s data platform.
• Own end-to-end data platform across BigQuery, Dataflow, Airflow, and ingestion tools.
• Model data with dimensional modeling and LookML.
• Triage data quality and performance issues and drive fixes.
• Mentor engineers and analysts, provide code reviews and feedback.
• Collaborate with product teams and clients to align data initiatives.

Skills

Data platform architecture

Leadership & mentoring

Cross-team collaboration

Communication with executives

Problem solving

Project estimation & planning

Cost optimization mindset

Tools

BigQuery

Dataflow

Airflow

Cloud Run

Cloud Functions

Fivetran

Looker/LookML

CI/CD pipelines

SQL

Job description

Role Highlights

• Set the technical direction for Seer’s data platform by defining the architecture and the standards that other engineers build toward.

• Own Seer’s data platform end-to-end across BigQuery, Dataflow, Airflow, Cloud Run, Cloud Functions, Cloud Storage, and ingestion tools like Fivetran, while making the cost, latency, and freshness tradeoffs that let us scale without surprises.

• Set the standard for how we model and serve data. This runs from dimensional modeling (grain, conformed dimensions, slowly changing dimensions) and dbt incremental models to a governed LookML semantic layer with row-level security, surfaced in our embedded Looker interface and via MCP.

• Structure data for agents to retrieve correct, deterministic answers to natural language questions from our non-technical team members.

• Automate the routine work around dbt: generating tests and documentation, assessing the downstream impact of schema changes, and producing migration or backfill plans.

• Build triage agents that read pipeline failures and data quality alerts, trace the cause through logs and lineage, and fix the issue or recommend a fix within established operational guardrails.

• Tune what’s expensive: evolve the observability layer from a manual process to ai-powered workflows that hunt down the slow query, the redundant DAG, and the leaky storage policy. Cut BigQuery costs and dashboard load times, and bring the team along with you.

• Document and teach. Write playbooks that survive system evolution, decision records that explain why not just what, and onboarding paths that help each new hire ramp up faster.

• Mentor data engineers and analysts across teams, pair on tough problems, and leave PR comments that make people better, not just their code.

Essential Skills

• Expert-level fluency across the GCP data stack, including **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 embedded dashboards that blend our built in-house SERP microservice data, CRM, and ad engine data into proprietary cross-platform data models. You write clean **SQL **and are just as good at untangling someone else’s.

• Expert Git and version control skills, which you use 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, including 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.

• Proactive and clear communication with project managers, supported by strong estimation skills to provide early visibility into deadline feasibility and guide decision-making.

• 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.

• 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.

This might not be the right role for you if...

• Marketing context feels foreign or uninteresting to you. At Seer, the why always traces back to a client, a campaign, or a strategist.

• You want to lead from the conference room. At Seer, leads stay close to the code.

• Mentorship feels like a tax rather than a craft. At Seer, making the team around you better is the job.

• You only feel comfortable leading with a reporting line behind you. This role earns its authority through expertise and trust. Titles are not the defining factor in your ability to lead.

• You see AI as hype rather than a practical tool.

• You find time management, context-switching, and juggling multiple priorities challenging.

• You struggle to adapt to new tools and processes.

30/60/90 Day Plan

• By Day 30, you will have completed division training, gotten oriented in our GCP and Looker environments, built relationships across the data engineering team, and shipped your first reviewed PR through our CI/CD pipeline.

• By Day 60, you will have led your first architectural decision (owning the design of a new data pipeline, model, or platform improvement), mentored engineers through code review, pair programming, and consulted on a custom engineering client project.

• By Day 90, you will be up and running at full capacity, owning the technical direction of the data platform, raising the data design bar across the team, and influencing how Seer builds and operates its data stack end-to-end.