Data Engineer (Salesforce / Snowflake Focus)
New
I
In All MediaData engineering
Location: Colombia / Costa Rica / Argentina / Peru / Nicaragua; Remote, US Central Time (CT)ContractSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- Bachelor’s degree with 5+ years of related data engineering experience, or a Master’s degree with 3+ years of experience.
- Required Skills
- AWSPythonSQLApache AirflowGitSalesforceSnowflakeCI/CD
Requirements
- Hold a Bachelor’s degree and have 5+ years of related data engineering experience, or hold a Master’s degree and have 3+ years of experience.
- Have strong SQL skills and hands-on proficiency in Python, Java, or Scala.
- Have extensive experience with relational, SQL, and NoSQL databases.
- Have production experience with Snowflake and AWS for modern data warehouses or lakehouses.
- Have deep expertise in Apache Airflow or an equivalent enterprise scheduler.
- Understand hourly and daily incremental loading, query and performance tuning, and distributed processing at scale.
- Have a strong foundation in dimensional and analytical data modeling, including scalable domain-oriented models.
- Have hands-on experience building ETL/ELT pipelines integrating AWS, Snowflake, and Salesforce data.
- Have experience with automated data validation, monitoring and alerting, Git version control, and CI/CD pipelines.
- Use AI-assisted coding tools such as Cursor or Claude.
- Align full-time business hours with US Central Time (CT).
- Use English for interviews, technical documentation, architectural reviews, team syncs, and operational communications.
Responsibilities
- Design, build, and maintain scalable batch pipelines with hourly and daily incremental loads into Snowflake and AWS data lakes.
- Integrate multi-source enterprise and SaaS data, including Salesforce data.
- Design and evolve dimensional and domain-oriented analytical data models for GTM functions and Salesforce integrations.
- Optimize distributed data processing and SQL query performance across large datasets.
- Implement automated data quality validation, pipeline testing, monitoring, and alerting.
- Maintain data engineering workflows within Git version-controlled CI/CD processes.
- Partner with Data Science and Machine Learning teams to productionize curated datasets and feature sets.
- Communicate architectural decisions, data contracts, and delivery progress with engineering peers and GTM stakeholders.
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