Data Engineer – Marketing Technology
New
J
JobgetherMarketing technology
Based in the United StatesFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- At least 5 years of experience in data engineering
- Required Skills
- PythonSQLOAuthSalesforceRESTful APIsDatabricksPySpark
Requirements
- At least 5 years of experience in data engineering, including hands-on development and production operation of data pipelines.
- Strong proficiency in SQL and Python/PySpark for data processing and pipeline development.
- Experience building and maintaining ETL/ELT workflows using Databricks, dbt, Airflow, Azure Data Factory, or equivalent tools.
- Hands-on experience with cloud data platforms such as Databricks, Snowflake, BigQuery, or Amazon Redshift.
- Understanding of dimensional data modeling, fact and dimension tables, schema design, and data warehouse concepts.
- Experience with medallion or layered data architectures, Kimball-style star schemas, and one-big-table modeling approaches.
- Working knowledge of API integration patterns, including REST, webhooks, OAuth, and batch synchronization.
- Experience with CRM platforms such as Salesforce or HubSpot, marketing automation systems, and CPQ or quoting tools such as DealHub or similar.
- Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent professional experience.
- Preferred: Experience with Delta Lake, PySpark, Unity Catalog, HubSpot APIs, and customer identity resolution and deduplication.
- Preferred: Familiarity with marketing data concepts, Azure cloud services, and data security and privacy practices for personally identifiable information.
Responsibilities
- Design, build, and maintain ETL/ELT pipelines using a medallion architecture to integrate data from business systems into Databricks.
- Develop data transformations using Python, PySpark, and SQL, following engineering standards and code review processes.
- Manage data synchronization between Databricks and HubSpot, enriching marketing records and bringing engagement data into the data warehouse.
- Build and maintain Exchange layer pipelines and scheduled batch jobs and event-driven integrations using REST APIs, webhooks, and OAuth.
- Monitor pipeline performance, implement alerts for failures and data quality issues, and lead incident resolution.
- Build dimensional data models, curated datasets, and data marts for business logic, KPI calculations, reporting, and campaign activation.
- Implement identity resolution, deduplication, normalization, validation, and data quality checks across customer profiles and marketing platforms.
- Develop data infrastructure for audience segmentation, lifecycle marketing, campaign attribution, funnel reporting, and marketing ROI measurement.
- Document data flows, integration architecture, operational procedures, and troubleshooting runbooks.
- Collaborate with marketing, sales, IT, and analytics stakeholders to translate business requirements into scalable data solutions.
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