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Jobgether

Data Engineer Client Data Operations

Remote, United States

$115,000 to $145,000 USD / year

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Job details

Pay: $115,000 to $145,000 USD / year
Experience: Several years of hands-on data engineering experience, ideally 4+ years, including building, maintaining, and operating production data pipelines.
Posting language: English

Original job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer Client Data Operations based in the United States.

The Data Engineer will play a key role in building, maintaining, and optimizing the data infrastructure that supports client marketing campaigns, audience management, and business reporting. This hands-on position focuses on onboarding new client data, transforming complex and inconsistent source files into reliable production-ready datasets, and ensuring data pipelines operate efficiently. You will take ownership of production workflows, troubleshoot technical issues, and develop scalable ingestion processes using modern cloud technologies. Working closely with analytics, campaign, account, and technical teams, you will help ensure data accuracy, security, and seamless campaign delivery. This is an individual contributor role offering significant autonomy and the opportunity to improve operational processes within a growing, data-driven environment. The position is fully remote across the United States, with a preference for candidates located in the Dallas, Texas area.

Accountabilities:

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Client Data Onboarding: Set up and manage incoming data feeds from client and vendor systems, including SFTP connections and scheduled file deliveries. Analyze source files, identify missing fields, inconsistent formats, and schema changes, and map incoming information into established database structures.

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Data Validation and Processing: Develop validation, identity matching, and data preparation processes, including record-count reconciliation, LTID assignment, and CASS/NCOA processing when applicable. Ensure data quality before it enters production workflows.

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Pipeline Development: Build, maintain, and optimize scalable data pipelines using SQL, Python, and Spark to support new direct mail and digital marketing clients. Establish reusable data structures and avoid unnecessary one-off solutions.

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Workflow Automation: Configure scheduled jobs, file monitoring, transformations, testing, and automated alerts to minimize manual intervention and improve pipeline reliability.

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Production Monitoring and Troubleshooting: Own daily monitoring and maintenance of Databricks workflows, AWS jobs, and related data tools. Investigate and resolve pipeline failures, schema changes, expired credentials, broken dependencies, stale data, and vendor-related issues.

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Campaign and Mail Production: Manage routine audience and direct mail production activities, including data suppression, address hygiene, record reconciliation, quality control, and preparation of files for external vendors, following established approval procedures.

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Operational Documentation: Create and maintain clear documentation covering data sources, transformations, validation processes, outputs, troubleshooting procedures, and recovery workflows.

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Data Security and Compliance: Identify sensitive information and apply approved privacy, access, and data-handling policies. Escalate ambiguous data governance or privacy questions to the appropriate stakeholders.

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Cross-Functional Collaboration: Work closely with the Senior Analytics Engineer to provide clean, consistent, well-documented datasets for downstream modeling, analysis, and reporting.

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Stakeholder Communication: Collaborate with campaign, account management, and technical teams to clarify requirements, resolve data issues, communicate delivery risks, and recommend appropriate solutions.

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Continuous Improvement: Identify opportunities to strengthen pipeline monitoring, automate recurring tasks, improve operational efficiency, and reduce dependency on manual troubleshooting.

Requirements:

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Several years of hands-on data engineering experience, ideally 4+ years, including building, maintaining, and operating production data pipelines.

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Strong proficiency in SQL and Python, with practical experience using Apache Spark or comparable large-scale data processing technologies.

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Experience with cloud-based data engineering environments, particularly Databricks and AWS or equivalent cloud platforms.

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Demonstrated ability to investigate unfamiliar, inconsistent, or poorly documented data sources and transform them into reliable production-ready feeds.

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Experience with data orchestration, workflow automation, monitoring, schema mapping, file-based integrations, and pipeline testing.

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Familiarity with version control systems such as Git, access management, operational documentation, and production support practices.

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Strong understanding of data quality principles, including record validation, deduplication, identity matching, reconciliation, and error detection.

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Experience handling personally identifiable information (PII) and protected health information (PHI), with an understanding of privacy and security requirements in regulated environments.

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Excellent analytical and problem-solving skills, with strong attention to detail and the ability to identify root causes rather than simply address symptoms.

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Ability to work independently, manage competing priorities, and take ownership of production issues in a fast-paced environment.

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Strong communication skills, including the ability to explain technical challenges and potential solutions to both technical and non-technical stakeholders.

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Sound professional judgment, particularly when handling sensitive information or situations requiring business-rule and privacy-related decisions.

Additional experience considered an advantage:

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Experience with direct mail data processing, digital marketing audience management, and campaign delivery workflows.

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Familiarity with CASS/NCOA, AccuZIP, AccuTrace, USPS tracking, audience deduplication, and suppression processes.

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Previous experience working with healthcare, Medicare, or health plan marketing data.

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Knowledge of SFTP, Amazon S3, Parquet, and client-facing reporting integrations.

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Experience working with digital marketing data sources and APIs.

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Residence in or around Dallas, Texas, is preferred but not required.

Benefits:

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Competitive annual base salary: $115,000–$145,000, depending on experience and qualifications.

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Fully remote working arrangement within the United States.

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Opportunity to take ownership of critical production systems and data engineering processes.

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Direct involvement in meaningful projects supporting healthcare, financial services, and other complex industries.

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Opportunity to work with modern data technologies, including SQL, Python, Spark, Databricks, and AWS.

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Professional development through hands-on technical challenges and collaboration with experienced analytics professionals.

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Opportunity to contribute to the development of scalable systems and improve established operational processes.

•

Collaborative environment within a growing, performance-driven team.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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More about this job

Skills

Reporting
Troubleshoot
Cloud technologies
SQL
Python
Apache Spark
Digital marketing
Workflow
Troubleshooting
Databricks
AWS
Quality control
Documentation
Communication skills
Process improvement
Data engineering
Process data
Control systems
Git
Problem solving
Attention to detail
Work independently

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