Data Engineering Lead
New
S
Seer InteractiveDigital marketing
We can only consider candidates based in the US, continental US-based hoursFull-TimeLead
Salary140,000 - 160,000 USD per year
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Job Details
- Required Skills
- SQLApache AirflowGitCI/CDBigQuerydbtLooker
Requirements
- Bring expert-level fluency across the GCP data stack, including BigQuery, Dataflow, Airflow, Cloud Run, Cloud Functions, Datastore, and Cloud Storage.
- Have a track record of architecting production pipelines end-to-end.
- Demonstrate expertise in BigQuery partitioning, clustering, materialized views, and cost optimization.
- Have expert command of modular dbt projects, reusable LookML, Looker explores, and embedded dashboards.
- Write clean SQL and be able to untangle existing queries.
- Bring expert Git and version control skills and experience designing CI/CD pipelines with multi-stage deploys and automated test gates.
- Be comfortable using AI and LLM tools, including crafting context-aware prompts, building evaluation harnesses, and integrating LLMs into engineering workflows.
- Communicate clearly with project managers and stakeholders and provide strong estimates and early visibility into deadline feasibility.
- Have a track record of leading through technical authority and mentoring engineers.
- Be comfortable staying close to code, adapting to new tools and processes, and managing multiple priorities.
Responsibilities
- Define the architecture and standards for Seer’s data platform.
- Own the data platform across BigQuery, Dataflow, Airflow, Cloud Run, Cloud Functions, Cloud Storage, and ingestion tools such as Fivetran.
- Model and serve data using dbt and a governed LookML semantic layer, surfaced through Looker and MCP.
- Structure data so agents can retrieve deterministic answers to natural-language questions.
- Automate dbt testing, documentation, schema-change impact assessments, and migration or backfill planning.
- Build triage agents to investigate pipeline failures and data quality alerts and fix issues or recommend fixes within operational guardrails.
- Improve observability, query performance, dashboard load times, and storage efficiency while reducing BigQuery costs.
- Document platform decisions and operating practices, and teach and mentor data engineers and analysts.
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