Data Engineer, Client Data Operations
New
J
JobgetherMarketing data
Fully remote across the United StatesFull-TimeMiddle
Salary115,000 - 145,000 USD per year
Apply NowOpens the employer's application page
Job Details
- Experience
- Several years of hands-on data engineering experience, ideally 4+ years
- Required Skills
- AWSPythonSQLGitSparkDatabricks
Requirements
- Several years of hands-on data engineering experience, ideally 4+ years, building, maintaining, and operating production data pipelines.
- Strong proficiency in SQL and Python.
- Practical experience with Apache Spark or comparable large-scale data processing technologies.
- Experience with cloud-based data engineering environments, particularly Databricks and AWS or equivalent cloud platforms.
- Experience with data orchestration, workflow automation, monitoring, schema mapping, file-based integrations, and pipeline testing.
- Familiarity with version control systems such as Git, access management, operational documentation, and production support practices.
- Strong understanding of data quality, including record validation, deduplication, identity matching, reconciliation, and error detection.
- Experience handling PII and PHI and understanding privacy and security requirements in regulated environments.
- Ability to investigate unfamiliar, inconsistent, or poorly documented data sources and transform them into reliable production-ready feeds.
- Ability to work independently, manage competing priorities, and take ownership of production issues.
- Direct mail processing, digital marketing audience management, CASS/NCOA, AccuZIP, AccuTrace, USPS tracking, or suppression experience is advantageous.
- Knowledge of SFTP, Amazon S3, Parquet, client-facing reporting integrations, digital marketing data sources, or APIs is advantageous.
Responsibilities
- Set up and manage incoming client and vendor data feeds, including SFTP connections and scheduled file deliveries.
- Analyze source files, identify missing fields, inconsistent formats, and schema changes, and map information into established database structures.
- Develop validation, identity-matching, and data-preparation processes, including record-count reconciliation, LTID assignment, and CASS/NCOA processing when applicable.
- Build, maintain, and optimize scalable data pipelines using SQL, Python, and Spark.
- Configure scheduled jobs, file monitoring, transformations, testing, and automated alerts.
- Monitor and maintain Databricks workflows, AWS jobs, and related data tools; investigate and resolve pipeline and vendor issues.
- Manage audience and direct mail production activities, including suppression, address hygiene, reconciliation, quality control, and vendor-file preparation.
- Document data sources, transformations, validation processes, outputs, troubleshooting procedures, and recovery workflows.
- Apply approved privacy, access, and data-handling policies, escalating ambiguous governance or privacy questions.
- Collaborate with analytics, campaign, account management, and technical teams to clarify requirements, resolve issues, and deliver reliable datasets.
View Full Description & ApplyYou'll be redirected to the employer's site
