Senior Data Engineer (Contract) – Build BigQuery Warehouse & ETL (LATAM Preferred)
Scope of Work – Project-Based Data Engineer (LATAM Preferred)
Project Overview
Forward Storage is seeking a contract-based Data Engineer to design, implement, and document a modern data warehouse and ETL/ELT architecture. The goal is to centralize operational, financial, sales, and marketing data into an analytics-ready warehouse to support Tableau/Power BI reporting.
This engagement is project-based with a clearly defined build phase, followed by optional light ongoing support.
Primary Objectives:
•Design and implement a scalable, low-maintenance data warehouse.
• Establish automated data pipelines from core SaaS platforms.
• Model data into analytics-ready fact and dimension tables.
• Ensure data accuracy, reliability, and documentation for long-term ownership by the internal analyst.
Initial Data Sources:
• Cubby – property management (financial & operational data)
• AppFolio – property management (financial & operational data)
• HubSpot – CRM, leads, sales funnel data
• Google Ads – campaign, spend, performance data
• Facebook Ads – campaign, spend, performance data
Preferred Technology Stack (Open to Vetting):
• Data Warehouse: Google BigQuery (preferred), Snowflake or equivalent acceptable
• ELT / Ingestion: Airbyte (preferred), Fivetran, Stitch, or equivalent
• Transformation Layer: dbt (Core or Cloud preferred)
• BI Tool: Tableau (preferred), others to be considered
• Version Control: GitHub or GitLab
Note: The engineer may recommend alternative tools if they better meet reliability, cost, or maintainability goals. Final stack selection will be mutually agreed upon.
Scope of Work
Phase 1 – Discovery & Architecture (1–2 weeks)
• Review available APIs, data schemas, and access methods for all source systems
• Recommend final warehouse and ELT architecture
• Define data ingestion strategy (incremental loads, refresh cadence)
• Establish naming conventions, schemas, and data modeling standards
• Define high-level data governance and quality approach
Phase 2 – Implementation & Modeling (3–5 weeks)
• Configure cloud data warehouse environment
• Build automated ELT pipelines for all Phase 1 data sources
• Create raw/staging tables with minimal transformation
• Develop transformed models including:
o Financial metrics by property and time
o Operational performance (occupancy, units, activity)
o Sales and funnel metrics from HubSpot
o Marketing spend and performance by channel
• Design analytics-ready fact and dimension tables
• Implement incremental refresh logic and basic data validation tests
Phase 3 – QA, Documentation & Handoff (1–2 weeks)
• Validate data accuracy with the internal analyst and stakeholders
• Optimize queries and model performance
• Deliver documentation including:
o Data dictionary
o Entity-relationship overview
o Pipeline refresh schedule
• Walkthrough and handoff to internal analyst
• Finalize Git repository and project artifacts
Out of Scope
• Advanced ML or predictive modeling
• Real-time streaming architecture (unless separately agreed)
• Ongoing dashboard development
• Long-term infrastructure monitoring beyond agreed retainer
Deliverables
• Production-ready data warehouse
• Automated ELT pipelines
• Analytics-ready data models
• Documentation and handoff materials
• Optional support transition plan
Timeline
• Estimated total duration: 6–8 weeks
Compensation & Engagement Model
• Fixed project budget: USD TBD
• Milestone-based payments preferred
• Optional ongoing support retainer: 5–10 hrs/month
Required Qualifications
• 5+ years of data engineering or analytics engineering experience
• Strong SQL and data modeling skills
• Experience with cloud data warehouses (BigQuery, Snowflake, etc.)
• Experience with ELT tools (Airbyte, Fivetran, dbt, etc.)
• Familiarity with SaaS data sources (CRM, Ads platforms, financial systems)
• Comfortable working independently in a remote environment
• Clear written and spoken English
Success Criteria
• Reliable, automated data refreshes
• Clean, documented, analytics-ready data models
• Minimal ongoing engineering dependency
• Smooth handoff to internal analyst
Apply Now
Apply Now
Project Overview
Forward Storage is seeking a contract-based Data Engineer to design, implement, and document a modern data warehouse and ETL/ELT architecture. The goal is to centralize operational, financial, sales, and marketing data into an analytics-ready warehouse to support Tableau/Power BI reporting.
This engagement is project-based with a clearly defined build phase, followed by optional light ongoing support.
Primary Objectives:
•Design and implement a scalable, low-maintenance data warehouse.
• Establish automated data pipelines from core SaaS platforms.
• Model data into analytics-ready fact and dimension tables.
• Ensure data accuracy, reliability, and documentation for long-term ownership by the internal analyst.
Initial Data Sources:
• Cubby – property management (financial & operational data)
• AppFolio – property management (financial & operational data)
• HubSpot – CRM, leads, sales funnel data
• Google Ads – campaign, spend, performance data
• Facebook Ads – campaign, spend, performance data
Preferred Technology Stack (Open to Vetting):
• Data Warehouse: Google BigQuery (preferred), Snowflake or equivalent acceptable
• ELT / Ingestion: Airbyte (preferred), Fivetran, Stitch, or equivalent
• Transformation Layer: dbt (Core or Cloud preferred)
• BI Tool: Tableau (preferred), others to be considered
• Version Control: GitHub or GitLab
Note: The engineer may recommend alternative tools if they better meet reliability, cost, or maintainability goals. Final stack selection will be mutually agreed upon.
Scope of Work
Phase 1 – Discovery & Architecture (1–2 weeks)
• Review available APIs, data schemas, and access methods for all source systems
• Recommend final warehouse and ELT architecture
• Define data ingestion strategy (incremental loads, refresh cadence)
• Establish naming conventions, schemas, and data modeling standards
• Define high-level data governance and quality approach
Phase 2 – Implementation & Modeling (3–5 weeks)
• Configure cloud data warehouse environment
• Build automated ELT pipelines for all Phase 1 data sources
• Create raw/staging tables with minimal transformation
• Develop transformed models including:
o Financial metrics by property and time
o Operational performance (occupancy, units, activity)
o Sales and funnel metrics from HubSpot
o Marketing spend and performance by channel
• Design analytics-ready fact and dimension tables
• Implement incremental refresh logic and basic data validation tests
Phase 3 – QA, Documentation & Handoff (1–2 weeks)
• Validate data accuracy with the internal analyst and stakeholders
• Optimize queries and model performance
• Deliver documentation including:
o Data dictionary
o Entity-relationship overview
o Pipeline refresh schedule
• Walkthrough and handoff to internal analyst
• Finalize Git repository and project artifacts
Out of Scope
• Advanced ML or predictive modeling
• Real-time streaming architecture (unless separately agreed)
• Ongoing dashboard development
• Long-term infrastructure monitoring beyond agreed retainer
Deliverables
• Production-ready data warehouse
• Automated ELT pipelines
• Analytics-ready data models
• Documentation and handoff materials
• Optional support transition plan
Timeline
• Estimated total duration: 6–8 weeks
Compensation & Engagement Model
• Fixed project budget: USD TBD
• Milestone-based payments preferred
• Optional ongoing support retainer: 5–10 hrs/month
Required Qualifications
• 5+ years of data engineering or analytics engineering experience
• Strong SQL and data modeling skills
• Experience with cloud data warehouses (BigQuery, Snowflake, etc.)
• Experience with ELT tools (Airbyte, Fivetran, dbt, etc.)
• Familiarity with SaaS data sources (CRM, Ads platforms, financial systems)
• Comfortable working independently in a remote environment
• Clear written and spoken English
Success Criteria
• Reliable, automated data refreshes
• Clean, documented, analytics-ready data models
• Minimal ongoing engineering dependency
• Smooth handoff to internal analyst
Apply Now
Apply Now