Data Engineer

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.