Data Engineer
Buenos Aires, Autonomous City of Buenos Aires, Argentina, Bogotá, Bogota D.C., Colombia
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
Visa Sponsorship
Read the full posting
What you’ll work on
Full postingThe Data Engineer 2 will join a fun, dynamic team to help solve integration and data problems relating to sports and entertainment.
The team is finalizing its target data architecture between two directions — a balanced on-prem/cloud modernization and a best-in-class cloud-native platform — so the right candidate will bring strong fundamentals that apply to both, along with depth in at least one.
Design and maintain automated ingestion pipelines from cloud and on-prem sources into Snowflake using ADF
Build, test, and document dbt data models layered on Snowflake
Implement dimensional (star/snowflake) data models optimized for Snowflake
Monitor and optimize ADF pipelines and dbt runs for reliability and freshness
Expose semantic layers and data marts in Snowflake for Power BI or Tableau
From the employer’s posting
The Data Engineer 2 will join a fun, dynamic team to help solve integration and data problems relating to sports and entertainment. This role will design, build, and maintain data pipelines using a modern data stack — combining data from cloud and on-premises systems, transforming it with dbt, and orchestrating workflows end to end. The position is responsible for ETL/ELT from many disparate systems into the data warehouse and offers the opportunity to own projects from beginning to end.
The team is finalizing its target data architecture between two directions — a balanced on-prem/cloud modernization and a best-in-class cloud-native platform — so the right candidate will bring strong fundamentals that apply to both, along with depth in at least one. Architectural guidance will be available, but this engineer must be able to implement data models effectively and independently. The right candidate will be motivated to learn, contribute to the team and organization, and grow along with the business.
Design, build, and maintain automated data ingestion pipelines from cloud and on-premises sources into Snowflake using Azure Data Factory (ADF)
Build, test, and document scalable data models and transformations in dbt layered directly on Snowflake.
Implement dimensional data models (Star/Snowflake schemas) optimized for Snowflake performance and cost efficiency.
Monitor, schedule, and optimize ADF pipelines, dataset triggers, and dbt runs to ensure high pipeline reliability and data freshness.
Prepare, structure, and expose semantic data layers and data marts in Snowflake for BI reporting tools (e.g., Power BI, Tableau).
What you’ll bring
All qualificationsCore experience
- Bachelor's degree in Information Systems, Computer Science, or a related field.
- 3–5 years of hands-on data engineering or analytics engineering experience.
- SQL & Modeling: Advanced SQL expertise and proven experience with dimensional data modeling concepts.
- Python & Cloud Storage: Proficient in Python scripting and familiar with Azure cloud storage patterns (ADLS Gen2, Azure Blob Storage).
- Experience integrating data layers with modern BI visualization tools (e.g., Power BI, Tableau).
Preferred experience
- Experience with Snowpark, Python UDFs, or Snowflake Native Apps.
Qualification wording
Bachelor's degree in Information Systems, Computer Science, or a related field.
3–5 years of hands-on data engineering or analytics engineering experience.
SQL & Modeling: Advanced SQL expertise and proven experience with dimensional data modeling concepts.
Python & Cloud Storage: Proficient in Python scripting and familiar with Azure cloud storage patterns (ADLS Gen2, Azure Blob Storage).
Experience integrating data layers with modern BI visualization tools (e.g., Power BI, Tableau).
Experience with Snowpark, Python UDFs, or Snowflake Native Apps.
Education & alternatives
Required Qualifications - Bachelor's degree in Information Systems, Computer Science, or a related field. - 3–5 years of hands-on data engineering or analytics engineering experience.
Tools in this posting
- Python
- SQL
- Azure
- dbt
- Power BI
- Snowflake
- Tableau
Source — Tool mentions in context
- Perform data cleansing, standardization, and staging using ADLS Gen2/Blob storage landing zones. - Write Python code (or Azure Functions/Snowpark routines) to handle complex API extractions, custom transformations, and automated utility tasks. Analytics Engineering & DevOps Practices
- SQL & Modeling: Advanced SQL expertise and proven experience with dimensional data modeling concepts. - Python & Cloud Storage: Proficient in Python scripting and familiar with Azure cloud storage patterns (ADLS Gen2, Azure Blob Storage). - Analytics Engineering / DevOps: Practical experience with Git, CI/CD pipeline automation for data code, and automated testing patterns.
- Snowflake SnowPro Core Certification or Azure Data Engineer Associate (DP-203) certification. - Experience with Snowpark, Python UDFs, or Snowflake Native Apps. - Advanced Azure ecosystem knowledge (Azure Key Vault, Managed Identities, Azure Functions, Azure DevOps).
- Azure Data Factory: Hands-on experience creating, configuring, monitoring, and troubleshooting ADF pipelines, linked services, integration runtimes, and parameterization. - SQL & Modeling: Advanced SQL expertise and proven experience with dimensional data modeling concepts. - Python & Cloud Storage: Proficient in Python scripting and familiar with Azure cloud storage patterns (ADLS Gen2, Azure Blob Storage).
Data Integration & Pipeline Development - Design, build, and maintain automated data ingestion pipelines from cloud and on-premises sources into Snowflake using Azure Data Factory (ADF). - Translate business requirements into efficient ELT workflows using ADF for orchestration/ingestion and dbt (Core or Cloud) for in-warehouse transformations.
Analytics Engineering & DevOps Practices - Apply CI/CD practices (e.g., Azure DevOps, GitHub Actions) for dbt model deployment, ADF ARM templates, and version control. - Implement automated data quality testing via dbt tests, custom Snowflake alerts, and ADF error handling.
- dbt: Demonstrated proficiency with dbt (Core or Cloud) for modular transformation, macro development, testing, and documentation. - Azure Data Factory: Hands-on experience creating, configuring, monitoring, and troubleshooting ADF pipelines, linked services, integration runtimes, and parameterization. - SQL & Modeling: Advanced SQL expertise and proven experience with dimensional data modeling concepts.
Preferred Qualifications - Snowflake SnowPro Core Certification or Azure Data Engineer Associate (DP-203) certification. - Experience with Snowpark, Python UDFs, or Snowflake Native Apps.
- Experience with Snowpark, Python UDFs, or Snowflake Native Apps. - Advanced Azure ecosystem knowledge (Azure Key Vault, Managed Identities, Azure Functions, Azure DevOps). Professional Skills
About the role The Data Engineer 2 will join a fun, dynamic team to help solve integration and data problems relating to sports and entertainment. This role will design, build, and maintain data pipelines using a modern data stack — combining data from cloud and on-premises systems, transforming it with dbt, and orchestrating workflows end to end. The position is responsible for ETL/ELT from many disparate systems into the data warehouse and offers the opportunity to own projects from beginning to end. The team is finalizing its target data architecture between two directions — a balanced on-prem/cloud modernization and a best-in-class cloud-native platform — so the right candidate will bring strong fundamentals that apply to both, along with depth in at least one. Architectural guidance will be available, but this engineer must be able to implement data models effectively and independently. The right candidate will be motivated to learn, contribute to the team and organization, and grow along with the business.
- Design, build, and maintain automated data ingestion pipelines from cloud and on-premises sources into Snowflake using Azure Data Factory (ADF). - Translate business requirements into efficient ELT workflows using ADF for orchestration/ingestion and dbt (Core or Cloud) for in-warehouse transformations. - Build, test, and document scalable data models and transformations in dbt layered directly on Snowflake.
- Translate business requirements into efficient ELT workflows using ADF for orchestration/ingestion and dbt (Core or Cloud) for in-warehouse transformations. - Build, test, and document scalable data models and transformations in dbt layered directly on Snowflake. - Monitor, schedule, and optimize ADF pipelines, dataset triggers, and dbt runs to ensure high pipeline reliability and data freshness.
- Build, test, and document scalable data models and transformations in dbt layered directly on Snowflake. - Monitor, schedule, and optimize ADF pipelines, dataset triggers, and dbt runs to ensure high pipeline reliability and data freshness. - Implement dimensional data models (Star/Snowflake schemas) optimized for Snowflake performance and cost efficiency.
- Apply CI/CD practices (e.g., Azure DevOps, GitHub Actions) for dbt model deployment, ADF ARM templates, and version control. - Implement automated data quality testing via dbt tests, custom Snowflake alerts, and ADF error handling. - Optimize Snowflake compute/storage costs, virtual warehouse configurations, query performance, and dbt execution times.
- Implement automated data quality testing via dbt tests, custom Snowflake alerts, and ADF error handling. - Optimize Snowflake compute/storage costs, virtual warehouse configurations, query performance, and dbt execution times. - Proactively identify pipeline bottlenecks, data drift, or failures and resolve unexpected data quality issues.
Ongoing Responsibilities - Monitor and maintain production ADF integrations, dbt job schedules, and Snowflake warehouse health. - Manage multiple data projects concurrently while consistently meeting sprint and delivery milestones.
- Snowflake: Strong hands-on experience with Snowflake architecture (virtual warehouses, staging, zero-copy cloning, tasks/streams, and performance tuning). - dbt: Demonstrated proficiency with dbt (Core or Cloud) for modular transformation, macro development, testing, and documentation. - Azure Data Factory: Hands-on experience creating, configuring, monitoring, and troubleshooting ADF pipelines, linked services, integration runtimes, and parameterization.
Business Intelligence & Data Analysis - Prepare, structure, and expose semantic data layers and data marts in Snowflake for BI reporting tools (e.g., Power BI, Tableau). - Identify new internal and external data sources, integrating them into the Snowflake ecosystem via ADF based on business demand.
- Analytics Engineering / DevOps: Practical experience with Git, CI/CD pipeline automation for data code, and automated testing patterns. - Experience integrating data layers with modern BI visualization tools (e.g., Power BI, Tableau). Preferred Qualifications
- Monitor, schedule, and optimize ADF pipelines, dataset triggers, and dbt runs to ensure high pipeline reliability and data freshness. - Implement dimensional data models (Star/Snowflake schemas) optimized for Snowflake performance and cost efficiency. - Perform data cleansing, standardization, and staging using ADLS Gen2/Blob storage landing zones.
- Prepare, structure, and expose semantic data layers and data marts in Snowflake for BI reporting tools (e.g., Power BI, Tableau). - Identify new internal and external data sources, integrating them into the Snowflake ecosystem via ADF based on business demand. - Collaborate with data analysts and business stakeholders to turn complex data requests into performant technical solutions.
- Manage multiple data projects concurrently while consistently meeting sprint and delivery milestones. - Maintain role-based access controls (RBAC), data masking, and governance in Snowflake to protect Personally Identifiable Information (PII) and ensure regulatory compliance (e.g., HIPAA). What you bring:
- 3–5 years of hands-on data engineering or analytics engineering experience. - Snowflake: Strong hands-on experience with Snowflake architecture (virtual warehouses, staging, zero-copy cloning, tasks/streams, and performance tuning). - dbt: Demonstrated proficiency with dbt (Core or Cloud) for modular transformation, macro development, testing, and documentation.
Posting record
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- First seen by us
- Oct 1, 2026 · 28 days ago
- Recorded sightings
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- Last seen by us
- Oct 29, 2026
- Employer says posted
- Sep 15, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
Build the Future with AspenView Technology Partners
At AspenView, we are passionate about transforming the way organizations approach technology. We specialize in creating high-performing, nearshore IT teams to help North American clients innovate faster and more efficiently.
As we continue to grow, we’re looking for exceptional people to join our team and help drive impactful change across industries.
Why Join AspenView?
At AspenView, we’re more than a nearshore IT partner—we’re a people-first, purpose-driven company that believes great culture drives great outcomes. We’re passionate about connecting talent and technology to deliver measurable value for clients—and meaningful career paths for our people.
Here’s what you can expect:
- Competitive base
- Comprehensive benefits and wellness support
- Flexible work model: hybrid, remote, or in-office
- Real growth opportunities and leadership visibility
- Inclusive, respectful culture that blends U.S. innovation with Colombian heart
- A company that listens, invests in you, and celebrates wins together
About the role
The Data Engineer 2 will join a fun, dynamic team to help solve integration and data problems relating to sports and entertainment. This role will design, build, and maintain data pipelines using a modern data stack — combining data from cloud and on-premises systems, transforming it with dbt, and orchestrating workflows end to end. The position is responsible for ETL/ELT from many disparate systems into the data warehouse and offers the opportunity to own projects from beginning to end.
The team is finalizing its target data architecture between two directions — a balanced on-prem/cloud modernization and a best-in-class cloud-native platform — so the right candidate will bring strong fundamentals that apply to both, along with depth in at least one. Architectural guidance will be available, but this engineer must be able to implement data models effectively and independently. The right candidate will be motivated to learn, contribute to the team and organization, and grow along with the business.
What you will do:
Data Integration & Pipeline Development
- Design, build, and maintain automated data ingestion pipelines from cloud and on-premises sources into Snowflake using Azure Data Factory (ADF).
- Translate business requirements into efficient ELT workflows using ADF for orchestration/ingestion and dbt (Core or Cloud) for in-warehouse transformations.
- Build, test, and document scalable data models and transformations in dbt layered directly on Snowflake.
- Monitor, schedule, and optimize ADF pipelines, dataset triggers, and dbt runs to ensure high pipeline reliability and data freshness.
- Implement dimensional data models (Star/Snowflake schemas) optimized for Snowflake performance and cost efficiency.
- Perform data cleansing, standardization, and staging using ADLS Gen2/Blob storage landing zones.
- Write Python code (or Azure Functions/Snowpark routines) to handle complex API extractions, custom transformations, and automated utility tasks.
Analytics Engineering & DevOps Practices
- Apply CI/CD practices (e.g., Azure DevOps, GitHub Actions) for dbt model deployment, ADF ARM templates, and version control.
- Implement automated data quality testing via dbt tests, custom Snowflake alerts, and ADF error handling.
- Optimize Snowflake compute/storage costs, virtual warehouse configurations, query performance, and dbt execution times.
- Proactively identify pipeline bottlenecks, data drift, or failures and resolve unexpected data quality issues.
Business Intelligence & Data Analysis
- Prepare, structure, and expose semantic data layers and data marts in Snowflake for BI reporting tools (e.g., Power BI, Tableau).
- Identify new internal and external data sources, integrating them into the Snowflake ecosystem via ADF based on business demand.
- Collaborate with data analysts and business stakeholders to turn complex data requests into performant technical solutions.
Ongoing Responsibilities
- Monitor and maintain production ADF integrations, dbt job schedules, and Snowflake warehouse health.
- Manage multiple data projects concurrently while consistently meeting sprint and delivery milestones.
- Maintain role-based access controls (RBAC), data masking, and governance in Snowflake to protect Personally Identifiable Information (PII) and ensure regulatory compliance (e.g., HIPAA).
What you bring:
Required Qualifications
- Bachelor's degree in Information Systems, Computer Science, or a related field.
- 3–5 years of hands-on data engineering or analytics engineering experience.
- Snowflake: Strong hands-on experience with Snowflake architecture (virtual warehouses, staging, zero-copy cloning, tasks/streams, and performance tuning).
- dbt: Demonstrated proficiency with dbt (Core or Cloud) for modular transformation, macro development, testing, and documentation.
- Azure Data Factory: Hands-on experience creating, configuring, monitoring, and troubleshooting ADF pipelines, linked services, integration runtimes, and parameterization.
- SQL & Modeling: Advanced SQL expertise and proven experience with dimensional data modeling concepts.
- Python & Cloud Storage: Proficient in Python scripting and familiar with Azure cloud storage patterns (ADLS Gen2, Azure Blob Storage).
- Analytics Engineering / DevOps: Practical experience with Git, CI/CD pipeline automation for data code, and automated testing patterns.
- Experience integrating data layers with modern BI visualization tools (e.g., Power BI, Tableau).
Preferred Qualifications
- Snowflake SnowPro Core Certification or Azure Data Engineer Associate (DP-203) certification.
- Experience with Snowpark, Python UDFs, or Snowflake Native Apps.
- Advanced Azure ecosystem knowledge (Azure Key Vault, Managed Identities, Azure Functions, Azure DevOps).
Professional Skills
- Exceptional attention to detail and strong commitment to data quality.
- Clear verbal and written communication skills across technical and non-technical audiences.
- Strong organizational skills with the ability to prioritize and self-manage in a fast-paced environment.
Visa Sponsorship
AspenView does not sponsor employment visas for this role. Applicants must be permanently authorized to work in their country of residence and must not require visa sponsorship now or in the future.
Equal Opportunity Employer
AspenView is proud to be an equal opportunity employer. We believe in creating an environment where all employees feel welcome, valued, and empowered to succeed. We celebrate diversity and strive to build a culture of inclusion where all individuals, regardless of their race, color, gender, gender identity or expression, sexual orientation, disability, age, or any other characteristic, can thrive. We encourage applicants from all walks of life to join our team and make a lasting impact.
Source details
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Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Buenos Aires, Autonomous City of Buenos Aires, Argentina, Bogotá, Bogota D.C., Colombia
- Comprehensive benefits and wellness support - Flexible work model: hybrid, remote, or in-office - Real growth opportunities and leadership visibility
- Work authorization
- Strong organizational skills with the ability to prioritize and self-manage in a fast-paced environment. Visa Sponsorship AspenView does not sponsor employment visas for this role. Applicants must be permanently authorized to work in their country of residence and must not require visa sponsorship now or in the future. Equal Opportunity Employer
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