Data Engineer (CRM)
New
J
JobgetherData Engineering
Brazil and other LATAM locations, Must maintain at least six hours of overlap with US Central Time business hoursContractMiddle
Salary not disclosed
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Job Details
- Languages
- English
- Required Skills
- AWSPythonSQLApache AirflowGCPSnowflakeData modelingdbtCRM
Requirements
- Advanced proficiency in SQL with extensive hands-on experience using Snowflake.
- Strong Python programming skills for data processing, transformation, and automation.
- Experience developing ETL/ELT pipelines using tools such as dbt, Apache Airflow, or equivalent orchestration frameworks.
- Practical experience working with cloud data platforms, preferably Google Cloud Platform (GCP) or AWS.
- Solid understanding of CRM data structures, including leads, contacts, accounts, and opportunities.
- Experience with data modeling, analytics engineering, and enterprise data management practices.
- Ability to communicate clearly in English, as interviews, documentation, meetings, and daily collaboration will be conducted in English.
- Availability to work aligned with US Central Time business hours, maintaining at least six hours of overlap with distributed teams.
Responsibilities
- Build, optimize, and maintain reliable ETL/ELT data pipelines across Snowflake, Salesforce Data Cloud, and cloud platforms.
- Develop and maintain Go-To-Market (GTM) data models, including advertiser identity resolution, lead enrichment, customer relationships, and revenue analytics datasets.
- Write, optimize, and troubleshoot complex SQL queries and domain-specific queries to support reporting and analytics requirements.
- Implement data quality processes, schema management practices, and governance standards across enterprise data environments.
- Collaborate with data engineering teams and product managers to deliver technical requirements within agile project workflows.
- Support CRM data architecture initiatives, ensuring efficient integration between business systems and analytical platforms.
- Continuously improve pipeline performance, scalability, and reliability through engineering best practices.
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