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Data Engineer – Analytics, Modeling
In All MediaSenior Data Engineer focusing on analytics and modeling to modernize data platforms in LATAM. Collaborating with engineering teams to build scalable data solutions for high-impact digital products.
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
SQLPythondata modelingdata transformationdbtdata validationdata quality frameworksdata pipelinesdata orchestrationdata processing workflows
Soft Skills
analytical leadershipcross-functional collaborationproblem-solvingcommunication
Tools & Technologies
SnowflakeAWSS3EC2DagsterApache Airflow
Industry Keywords
data engineeringadvanced analyticslegacy data workflowspipeline healthtransactional datamatching algorithmsuser-behavior metrics
Tech Stack
Tools & technologiesAirflowApacheAWSCloudEC2PythonSQL
About the role
Key responsibilities & impact- Provide critical technical execution and analytical leadership, acting as a driving force in translating raw data into robust, production-ready data models.
- Ensure cross-functional teams have seamless access to un-compromised, highly performant, and real-time datasets.
- Responsible for dismantling legacy data workflows, engineering scalable data pipelines, and establishing rigorous validation standards to guarantee data reliability and pipeline health.
Requirements
What you’ll need- 3 to 5+ years of professional experience in dedicated data-focused engineering or advanced analytics roles.
- Exceptional ability to write, debug, and tune complex SQL queries for heavy data transformations, validation, and analytics reporting.
- Strong hands-on experience using Python to build custom data processing workflows, automation scripts, and pipeline connectors.
- Direct experience designing, managing, and indexing performant data models natively within Snowflake.
- Production experience developing modular, tested, and documented data transformation code using dbt.
- Practical familiarity with AWS cloud environments (e.g., S3, EC2, or related data orchestration services) supporting scalable storage and execution.
- Strong understanding of data validation, data quality frameworks, and pipeline health troubleshooting.
- Familiarity with next-generation data orchestrators like Dagster or Apache Airflow (Nice to have).
- Prior experience working with marketplace dynamics, transactional data, matching algorithms, or user-behavior metrics (Nice to have).
Benefits
Comp & perks- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Professional development opportunities