Global Data Engineer

Who We Are

The company is one of AdAge's 2026's BEST PLACES TO WORK !  As an independent and remote-first media and marketing services firm with a 90+ year history, the company has the security of an established company combined  with a start-up feel.  With leading data-driven and technology-enabled digital media and marketing services firm with teams in the U.S. and the UK, our diverse team of digital strategists, media practitioners, technologists and data scientists have won the most prestigious awards for innovative marketing technology, impactful work and inclusive remote-first places to work including being honored as a multiyear winner in Ad Age Best Places to Work, Ad Exchanger's Best Use of Technology by an Agency Award and two MarTech Breakthrough Awards and a certified service partner to The Trade Desk.

Please Note: We are targeting individuals that live in Brazil, Colombia, and Mexico for this role. We are not currently accepting candidates from the United States.

The company is seeking an experienced Senior Data Engineer to help design, build, and optimize the data infrastructure powering our analytics, technology platforms, and marketing solutions.

In this role, you'll take ownership of complex data engineering initiatives, from designing scalable data architectures and developing reliable ETL/ELT pipelines to optimizing warehouse performance, data quality, and cloud infrastructure costs.

You'll work closely with Product, Engineering, Analytics, and Data Science teams to ensure our data systems are scalable, efficient, and reliable. We're looking for someone who enjoys solving complex technical challenges, takes initiative, and can independently evaluate architectural decisions while remaining hands-on with development.

Our primary technology stack includes Snowflake, Apache Airflow, Python, and AWS . Experience with large-scale datasets, data modeling, and performance optimization will be essential to success in this role.

What You Will Do

Data Architecture & Engineering

• Design, develop, and maintain scalable data architectures, warehouses, and ETL/ELT pipelines supporting analytics, reporting, and technology products.

• Build and optimize data pipelines using Python, Apache Airflow, Snowflake, and AWS.

• Develop and maintain data models, schemas, and taxonomies to ensure consistency, accessibility, and usability across data systems.

• Evaluate architectural approaches, identify technical tradeoffs, and recommend solutions that support scalability and long-term maintainability.

• Integrate structured and unstructured data from APIs, databases, advertising platforms, and other internal and external sources.

• Support the development and evolution of data infrastructure used by analytics, machine learning, and AI applications.

Performance, Optimization & Reliability

• Optimize Snowflake warehouse performance, query execution, storage, and compute utilization to improve processing speed and manage costs.

• Identify bottlenecks across data pipelines and cloud infrastructure, implementing improvements that increase efficiency and reliability.

• Develop automated testing, data validation, and monitoring processes to maintain data accuracy and integrity.

• Troubleshoot production issues, investigate data discrepancies, and implement solutions that prevent recurring failures.

• Establish and maintain best practices for data quality, observability, documentation, and pipeline reliability.

• Evaluate new tools, technologies, and approaches that improve engineering efficiency and platform performance.

Collaboration & Technical Ownership

• Partner with Product, Engineering, Analytics, and Data Science teams to translate business requirements into scalable technical solutions.

• Take ownership of engineering initiatives from initial design through implementation, testing, deployment, and ongoing optimization.

• Collaborate with stakeholders to understand how data is used across the organization and ensure solutions support downstream business needs.

• Participate in technical design discussions, code reviews, and architectural planning.

• Contribute to engineering standards, documentation, and continuous improvement initiatives.

• Provide technical guidance and share knowledge with other engineers.

What We're Looking For

Required Qualifications

• 5+ years of professional experience in data engineering, software engineering, or related technical roles, including substantial hands-on experience with production data systems.

• Strong proficiency in Python and SQL , including building and optimizing complex data pipelines and transformations.

• Hands-on experience with Snowflake , including data warehousing, data modeling, query optimization, and performance tuning.

• Experience designing, building, and maintaining production ETL/ELT pipelines using Apache Airflow or comparable orchestration frameworks.

• Practical experience with AWS services supporting cloud-based data infrastructure.

• Demonstrated experience designing scalable data architectures, data models, and warehouse structures.

• Understanding of dimensional modeling, relational databases, data schemas, and data transformation techniques.

• Experience implementing automated testing, data validation, monitoring, and troubleshooting processes.

• Ability to identify performance bottlenecks, optimize data processing, and make informed decisions around infrastructure efficiency and costs.

• Experience independently owning technical solutions from design through production.

• Strong problem-solving skills and the ability to communicate technical decisions clearly with engineering and business stakeholders.

• Experience working collaboratively with distributed, cross-functional teams.

Preferred Qualifications

• Experience working with advertising technology (AdTech), marketing technology (MarTech), digital media, or agency data environments .

• Experience with financial technology (FinTech) or other industries managing complex, high-volume datasets.

• Familiarity with advertising and marketing data sources, including campaign performance, attribution, customer segmentation, and measurement.

• Experience with data clean rooms, privacy-conscious data collaboration, or large-scale data-sharing environments.

• Experience designing and maintaining data taxonomies, metadata frameworks, and data governance practices.

• Familiarity with dbt, Spark, Databricks, or similar data transformation and processing tools .

• Experience with Rust or an interest in working with additional programming languages.

• Experience supporting machine learning, AI, or advanced analytics initiatives.

• Familiarity with CI/CD practices, infrastructure as code, and cloud cost management.

Check us out at [link for members] to learn more!

If you identify as a female candidate, and feel you can do this role even if there are a few things perhaps you've not done, please apply anyway!  The company is 70% Female!  We realize that men tend to apply for jobs when they can meet around 60% of the requirements for the role, where women tend to only apply when they know they meet 100% of the requirements.

The company is human-first, constantly working to become more inclusive and to make sure our employee population reflects our desire to constantly add to our diversity in all ways.  We want applications from everyone , regardless of race, creed, color, religion, sex, sexual orientation, gender identity, national origin, marital status, citizen status, age, disability, military or protected veteran status, genetic predisposition or carrier status or any other legally protected status.