Gardner Resources Consulting, LLC
We’re seeking a
Lead Analytics Engineer
to help design, model, and scale a modern data environment for a global software organization. The company manages large volumes of data across multiple business units, and this role will play a key part in organizing and maturing that landscape as part of a multi-year strategic roadmap.
This position is ideal for a senior-level analytics engineer who can architect data solutions, build robust models, and stay hands‑on with development.
Role Focus
Architect and build new data models using
dbt
and modern modeling techniques.
Partner closely with leadership and business teams to translate complex requirements into technical solutions.
Support initiatives focused on
Finance and Payments
data domains.
Drive structure and clarity within a growing analytics ecosystem.
Technical Environment
Primary Data Warehouse:
BigQuery (mandatory)
Nice to Have:
Snowflake, Redshift
Orchestration:
Airflow (GCP Composer)
Languages:
Expert‑level SQL / dbt; strong Python required
Other Tools:
GCP or AWS, Fivetran, Apache Beam, Looker or Preset, Docker
Version Control:
Git / CI‑CD
Quality Tools:
dbt‑Elementary, dbt‑Osmosis, or Great Expectations preferred
Responsibilities Business Stakeholder Engagement
Gather and document complex business requirements.
Serve as a trusted data partner across multiple departments.
Data Modeling & Transformation
Design and implement robust, reusable data models within the warehouse.
Develop and maintain SQL transformations in dbt.
Optimize existing models and queries for performance, cost‑efficiency, and maintainability.
Build and maintain reliable data pipelines in collaboration with data engineering.
Utilize orchestration tools (Airflow) to manage and monitor workflows.
Manage and support dbt environments and transformations.
Data Quality & Governance
Implement validation checks and quality controls to ensure data integrity.
Define and enforce data governance best practices, including lineage and access control.
Enable Data Democratization & Self‑Service Analytics
Curate and prepare datasets for analysts, business users, and data scientists.
Develop semantic layers for consistent and accessible reporting.
Qualifications
Bachelor’s degree in Economics, Mathematics, Computer Science, or related field.
10+ years of experience in an Analytics Engineering role.
Expert in SQL and dbt with demonstrated modeling experience (Vault, 3NF, Dimensional).
Hands‑on experience with BigQuery or other cloud data warehouses.
Proficiency in Python and Docker.
Experience with Airflow (Composer), Git, and CI/CD pipelines.
Strong attention to detail and communication skills; able to interact with both technical and business stakeholders.
Experience in financial services or payments is a plus but not required.
Seniority level Not Applicable
Employment type Contract
Job function Engineering and Information Technology
Industries IT System Custom Software Development
#J-18808-Ljbffr
Lead Analytics Engineer
to help design, model, and scale a modern data environment for a global software organization. The company manages large volumes of data across multiple business units, and this role will play a key part in organizing and maturing that landscape as part of a multi-year strategic roadmap.
This position is ideal for a senior-level analytics engineer who can architect data solutions, build robust models, and stay hands‑on with development.
Role Focus
Architect and build new data models using
dbt
and modern modeling techniques.
Partner closely with leadership and business teams to translate complex requirements into technical solutions.
Support initiatives focused on
Finance and Payments
data domains.
Drive structure and clarity within a growing analytics ecosystem.
Technical Environment
Primary Data Warehouse:
BigQuery (mandatory)
Nice to Have:
Snowflake, Redshift
Orchestration:
Airflow (GCP Composer)
Languages:
Expert‑level SQL / dbt; strong Python required
Other Tools:
GCP or AWS, Fivetran, Apache Beam, Looker or Preset, Docker
Version Control:
Git / CI‑CD
Quality Tools:
dbt‑Elementary, dbt‑Osmosis, or Great Expectations preferred
Responsibilities Business Stakeholder Engagement
Gather and document complex business requirements.
Serve as a trusted data partner across multiple departments.
Data Modeling & Transformation
Design and implement robust, reusable data models within the warehouse.
Develop and maintain SQL transformations in dbt.
Optimize existing models and queries for performance, cost‑efficiency, and maintainability.
Build and maintain reliable data pipelines in collaboration with data engineering.
Utilize orchestration tools (Airflow) to manage and monitor workflows.
Manage and support dbt environments and transformations.
Data Quality & Governance
Implement validation checks and quality controls to ensure data integrity.
Define and enforce data governance best practices, including lineage and access control.
Enable Data Democratization & Self‑Service Analytics
Curate and prepare datasets for analysts, business users, and data scientists.
Develop semantic layers for consistent and accessible reporting.
Qualifications
Bachelor’s degree in Economics, Mathematics, Computer Science, or related field.
10+ years of experience in an Analytics Engineering role.
Expert in SQL and dbt with demonstrated modeling experience (Vault, 3NF, Dimensional).
Hands‑on experience with BigQuery or other cloud data warehouses.
Proficiency in Python and Docker.
Experience with Airflow (Composer), Git, and CI/CD pipelines.
Strong attention to detail and communication skills; able to interact with both technical and business stakeholders.
Experience in financial services or payments is a plus but not required.
Seniority level Not Applicable
Employment type Contract
Job function Engineering and Information Technology
Industries IT System Custom Software Development
#J-18808-Ljbffr