Data Engineer

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Join Hotspex Media! 🙌 #1 Ranked Media Buying and Planning Agency on Clutch.co 🔥 Finalist 'Best AI Tool', 2024 Digiday Technology Awards 🎉 Hybrid Work Model (1 Day in Office / Week) 🥇 Winner of Waterstone Canada's Most Admired Corporate Cultures Reports to: Director of AI & ML Location: Hybrid with the option for Remote if Outside Greater Toronto Area (must be legally authorized to work in and based in Canada) Team: Small, high-autonomy team with direct access to leadership. Impact: Owns design, build, operation of Hotspex's data transformation and storage layer. About the Role & Mission Connect data across BigQuery, Postgres, and Airtable; expose clean datasets to AI, Workflow, Analytics consumers Build and maintain dbt models transforming marketing platform data into conformed dimensional schemas (Kimball facts/dimensions) Own SQL surface: queries, stored procedures, views, materialized views, scheduled routines Optimize warehouse performance and cost: query tuning, partitioning, clustering, incremental models Orchestrate pipelines with Airflow or similar Core Competencies SQL Engineering: Writes, tunes, maintains complex SQL across BigQuery and Postgres Stored Procedures & Routines: Designs and owns stored procedures, scripted procedures, UDFs, scheduled jobs dbt / Transformation Modeling: Builds and maintains dbt models with tests, docs, incremental patterns Pipeline Orchestration: Schedules and monitors pipelines via Airflow or similar Cross-Functional Partnership: Delivers consumable data products for AI, Workflow, Analytics Job Specific Competencies Advanced SQL : Complex joins, window functions, CTEs, query optimization, execution plans on BigQuery and Postgres Stored Procedures & Routines: Production stored procedures, scripted procedures (BigQuery scripting / PL/pgSQL), UDFs, scheduled queries with error handling, idempotency, observability dbt Modeling: Sources, staging, intermediate, marts; tests; documentation; incremental strategies; macros Pipeline Orchestration: Airflow, Dagster, Prefect, or equivalent Data Modeling : Kimball facts/dimensions, slowly changing dimensions, conformed schemas Warehouse Optimization : Partitioning, clustering, materialized views, cost tuning on BigQuery Airtable Integration : Schema mapping, sync patterns, base-as-source Job Responsibilities Connect & Optimize Data Own connectivity between BigQuery, Postgres, and Airtable; ensure consumers (AI, Workflow, Analytics) get the schema they need Refactor ad-hoc SQL into versioned, tested, documented routines Optimize cost and performance: partitioning, clustering, materialization Detect and fix performance regressions before downstream impact SQL & Stored Procedure Ownership Own every production stored procedure, scripted procedure, scheduled query across BigQuery and Postgres Author new stored procedures for batch transforms, reporting routines, AI/ML feature prep Maintain stored-procedure inventory with ownership, dependencies, runbooks dbt Model Build & Maintenance Design schemas and write dbt models transforming marketing platform data (Google Ads, Meta, LinkedIn, etc.) into conformed dimensional schemas Implement dbt tests (uniqueness, not-null, referential integrity, custom rules) on every production model Maintain incremental models for high-volume tables; tune for cost and freshness Own dbt documentation and lineage Pipeline Orchestration Schedule, monitor, and version pipelines in Airflow or similar Alert routing, retry policy, backfill patterns Coordinate with Workflow Eng on hand-off points between n8n and orchestrated data pipelines Data Quality, Monitoring & Reliability Implement automated tests (dbt tests, freshness checks, row-count anomaly detection) Detect and acknowledge data quality incidents within 1 business hour (SLA) Author runbooks for common failure modes Track and reduce incident frequency; report trends quarterly Cross-Functional Partnership Partner with Workflow Automation Engineer on ingestion contracts: landing schemas, refresh patterns Partner with Junior AI Engineer on data needs for RAG, embeddings, AI services: feature tables, serving views Translate PM/CS and Product requirements into dimensional models Owns: SQL design, stored procedure logic, transformation modeling, performance choices Does not own: automation logic (Workflow Eng), AI service code (Jr AI Eng), client-facing strategy Documentation & Knowledge Use Claude Code for stored procedure docs, model READMEs, schema references Version-controlled repos, clean Markdown, proper Git hygiene Document data contracts: ingestion → transformation → consumption Continuous Improvement Use AI tooling (Claude Code, Cursor) to accelerate SQL authoring, refactoring, documentation Track and report query cost reduction and model freshness improvement quarterly Resolve categories of technical debt: consolidating duplicated SQL, retiring shadow tables Explicitly Out of Scope n8n automation de

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