Analytics Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an Analytics Engineer on a contract basis, requiring 3–5+ years of analytics/data engineering experience, expert SQL skills, and hands-on experience with Dataform or dbt. Pay rate and work location are unspecified.
🌎 - Country
United States
💱 - Currency
Unknown
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💰 - Day rate
-
🗓️ - Date discovered
August 8, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Unknown
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#SQL (Structured Query Language) #BI (Business Intelligence) #Datasets #Strategy #BigQuery #dbt (data build tool) #Data Engineering #Clustering #Storage #GIT #Documentation #Macros
Role description
Job Description:
Modeling & ELT: Design staged mart layers in BigQuery: using Dataform :(naming, conventions, macros).
Metrics & semantics: Define business-ready metrics (grain, SCD strategy, time zones, null handling) and deliver consistent calculations to downstream apps.
Quality & contracts: Implement tests (uniqueness, referential integrity, freshness), SLAs, and schema change policies; add lineage and documentation.
Performance & cost: Tune queries, partitions/clusters, and storage formats; track spend and p95/p99 latencies.
Collaboration: Partner with Data Eng, MLOps, and Internal Tools to align APIs, schemas, and release schedules; gather stakeholder feedback and iterate.
Governance: Apply RBAC, PII handling, and auditability; participate in reviews and incident postmortems.
Minimum qualifications:
3–5+ years in analytics/data engineering with production SQL modeling.
Expert SQL: on columnar warehouses (BigQuery preferred): partitioning, clustering, UDFs.
Hands-on with Dataform: or dbt: (tests, docs, macros, environments).
Strong documentation habits; comfort with Git-based development and CI/CD.
Proven track record delivering trusted datasets for BI/apps with SLAs.
Job Description:
Modeling & ELT: Design staged mart layers in BigQuery: using Dataform :(naming, conventions, macros).
Metrics & semantics: Define business-ready metrics (grain, SCD strategy, time zones, null handling) and deliver consistent calculations to downstream apps.
Quality & contracts: Implement tests (uniqueness, referential integrity, freshness), SLAs, and schema change policies; add lineage and documentation.
Performance & cost: Tune queries, partitions/clusters, and storage formats; track spend and p95/p99 latencies.
Collaboration: Partner with Data Eng, MLOps, and Internal Tools to align APIs, schemas, and release schedules; gather stakeholder feedback and iterate.
Governance: Apply RBAC, PII handling, and auditability; participate in reviews and incident postmortems.
Minimum qualifications:
3–5+ years in analytics/data engineering with production SQL modeling.
Expert SQL: on columnar warehouses (BigQuery preferred): partitioning, clustering, UDFs.
Hands-on with Dataform: or dbt: (tests, docs, macros, environments).
Strong documentation habits; comfort with Git-based development and CI/CD.
Proven track record delivering trusted datasets for BI/apps with SLAs.
