We are rebuilding the foundations of our data platform: moving from a fragmented setup toward a cloud-centered lakehouse, standardizing transformations with dbt, orchestrating with Airflow and preparing for a major possible change of event flows and identity modeling. You will be the senior hands-on engineer who turns this into a reliable, documented, testable data foundation that powers our tools and analytical/predictive systems.

Context you’ll step into:

• Current data landscape includes multiple layers, historically involving tools like Snowflake and a BigQuery reporting layer; target is to unify layers and governance around cloud, standardize ELT in dbt, and orchestrate reliably.
• We must also integrate more product telemetry (carousel/promo exposure, etc.) and support CRM activation pipelines.

How you’ll work:

You will work in a tight triangle:

• Senior Data Engineer (this role): owns data modeling, dbt transformations, BigQuery performance, pipeline correctness, and analytics “source of truth”.
• AI Infrastructure & DevOps Engineer: owns cloud infrastructure, CI/CD patterns, deployment mechanics, security, and platform reliability.
• Data Scientists / Analytics users: define business logic needs, validate outputs, and consume marts/features.
• Expectation: very close synchronization with the AI Infra/DevOps engineer on the end-to-end flow (environments, scheduling, monitoring, deployment workflow). You won’t be the CI/CD owner, but you’ll be the person who ensures the data layer is correct, scalable, and production-grade, and that it plugs cleanly into the existing engineering standards.

What you’ll do:

Build the Cloud lakehouse

• Implement a clear layered model (00 raw/landing, staging, intermediate, marts/30).
• Co-build with AI Infra engineer, scalable BigQuery datasets: partitioning/clustering strategies, cost/performance optimization.
• Define canonical business tables for our business needs

Standardize transformations with dbt

• Implement dbt project structure, naming conventions, documentation, tests.
• Build incremental models, snapshot patterns (SCD), and clear semantic definitions.
• Enforce quality gates: schema tests, uniqueness, freshness, anomaly checks.

Orchestration & reliability (Airflow / Cloud Composer)

• Productionize DAGs for ingestion + dbt runs + monitoring.
• Add observability: SLAs, retries, alerting, backfills, lineage.
• Ensure reliable refresh cadence for our tools and ML flows.

Ingestion & integration (including migration readiness)

• Build/own ingestion patterns from source systems into BigQuery
• Prepare for the new platform migration: adapt pipelines to new event schemas and identity model; maintain continuity for analytics and ML.

Must-have skills

• Senior-level SQL + data modeling (dimensional + analytics engineering patterns).
• Strong Cloud Data Lake expertise (BigQuery, Databricks, Snowflake, etc.)
• dbt in production: tests, docs, incremental models, CI checks.
• Airflow: DAG design, retries, backfills, alerts.
• Strong Python for pipeline utilities, data validation, and integration glue.
• Production mindset: monitoring, incident handling, reproducibility, documentation.
• Prompt engineering (hands-on): Ability to design, iterate, and evaluate prompts for real business outputs.

Nice-to-have

• Experience migrating from mixed stacks into a unified cloud architecture
• Experience with event/streaming data models.
• Data Science and analytics experience
• Experience with Power BI
• Agentic AI experience: Familiarity with agentic workflows (LLMs that use tools) to automate parts of analytics/ops. Experience with concepts like tool use, memory/state and RAG is a plus.

Date the job becomes available: March 1, 2026

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