Analytics Engineer (Suwanee)
BlueSky Resource Solutions, Suwanee, GA, United States
The is a hybrid opportunity with 4-days onsite in Suwanee. Candidates MUST be local to the Atlanta area. This role is not open to C2C, OPT, or any Visa consideration. No vendor support of any kind allowed.
JOB DESCRIPTION
Core Job Requirements
5+ years of experience in analytics engineering, data engineering, or advanced business intelligence roles within a modern data stack environment
Advanced proficiency in SQL with demonstrated experience building performance, well-modeled analytical datasets
Hands-on experience with dbt for analytics-layer modeling, testing, and documentation
Experience working with cloud data warehouses, preferably Snowflake, including performance tuning and cost-aware design
Strong understanding of analytics engineering best practices, including:
Star and snowflake schemas
Incremental models
Fact / dimension separation
Medallion Architecture
Experience working in a CI/CD-driven analytics environment, including version control, code review, and deployment pipelines
Proven ability to partner closely with business stakeholders to translate requirements into trusted analytical assets
Bachelor’s degree in Analytics, Information Systems, Computer Science, Mathematics, Finance, or equivalent practical experience
Sales Operations Domain Expertise (Required Emphasis)
Proven experience supporting Sales Operations, Revenue Operations, or Commercial Analytics functions
Strong knowledge of sales and revenue data domains, including:
Pipeline and funnel metrics
Bookings, billings, and revenue recognition concepts
Quota, attainment, and compensation-related metrics
Forecasting, actuals vs. targets, and variance analysis
Experience integrating and modeling data from CRM platforms (e.g., Salesforce or similar)
Ability to design sales performance and executive dashboards that support:
GTM leadership
Capacity planning
Territory and account analytics
Comfort working with imperfect or evolving operational data and establishing trust through modeling, validation, and documentation
Commercial Real Estate / Data Center Experience (Strong Preference)
Experience working with commercial real estate, data center operations, or infrastructure?driven businesses preferred
Familiarity with real estate–specific concepts such as:
Building and location hierarchies
Lease, colocation, and contract structures
Capacity, utilization, and availability metrics
Experience modeling data across site, campus, region, and portfolio hierarchies
Ability to align sales analytics with physical asset constraints (e.g., capacity, power, occupancy) is a strong differentiator
Technical & Platform Skills
Advanced SQL and data modeling expertise
dbt (models, tests, macros, docs, exposures)
Snowflake (query optimization, warehouse usage patterns, role-based access awareness)
Experience integrating data from SaaS systems using ELT tools (e.g., Fivetran, Stitch, etc.)
Familiarity with metadata management, data quality checks, and analytical lineage
Professional & Communication Skills
Strong business acumen with the ability to frame data in commercial terms
Clear, concise communicator capable of working with:
Sales leadership
Data engineering and platform teams
Ability to write clear, durable documentation for models, metrics, and assumptions
High attention to detail with a strong bias toward accuracy, consistency, and auditability
Comfortable operating in a federated enterprise environment with multiple upstream systems
JOB DESCRIPTION
Core Job Requirements
5+ years of experience in analytics engineering, data engineering, or advanced business intelligence roles within a modern data stack environment
Advanced proficiency in SQL with demonstrated experience building performance, well-modeled analytical datasets
Hands-on experience with dbt for analytics-layer modeling, testing, and documentation
Experience working with cloud data warehouses, preferably Snowflake, including performance tuning and cost-aware design
Strong understanding of analytics engineering best practices, including:
Star and snowflake schemas
Incremental models
Fact / dimension separation
Medallion Architecture
Experience working in a CI/CD-driven analytics environment, including version control, code review, and deployment pipelines
Proven ability to partner closely with business stakeholders to translate requirements into trusted analytical assets
Bachelor’s degree in Analytics, Information Systems, Computer Science, Mathematics, Finance, or equivalent practical experience
Sales Operations Domain Expertise (Required Emphasis)
Proven experience supporting Sales Operations, Revenue Operations, or Commercial Analytics functions
Strong knowledge of sales and revenue data domains, including:
Pipeline and funnel metrics
Bookings, billings, and revenue recognition concepts
Quota, attainment, and compensation-related metrics
Forecasting, actuals vs. targets, and variance analysis
Experience integrating and modeling data from CRM platforms (e.g., Salesforce or similar)
Ability to design sales performance and executive dashboards that support:
GTM leadership
Capacity planning
Territory and account analytics
Comfort working with imperfect or evolving operational data and establishing trust through modeling, validation, and documentation
Commercial Real Estate / Data Center Experience (Strong Preference)
Experience working with commercial real estate, data center operations, or infrastructure?driven businesses preferred
Familiarity with real estate–specific concepts such as:
Building and location hierarchies
Lease, colocation, and contract structures
Capacity, utilization, and availability metrics
Experience modeling data across site, campus, region, and portfolio hierarchies
Ability to align sales analytics with physical asset constraints (e.g., capacity, power, occupancy) is a strong differentiator
Technical & Platform Skills
Advanced SQL and data modeling expertise
dbt (models, tests, macros, docs, exposures)
Snowflake (query optimization, warehouse usage patterns, role-based access awareness)
Experience integrating data from SaaS systems using ELT tools (e.g., Fivetran, Stitch, etc.)
Familiarity with metadata management, data quality checks, and analytical lineage
Professional & Communication Skills
Strong business acumen with the ability to frame data in commercial terms
Clear, concise communicator capable of working with:
Sales leadership
Data engineering and platform teams
Ability to write clear, durable documentation for models, metrics, and assumptions
High attention to detail with a strong bias toward accuracy, consistency, and auditability
Comfortable operating in a federated enterprise environment with multiple upstream systems