Data Engineer Client Data Operations

Limetree•Dallas, TX

2d•Remote

About The Position

Limetree is hiring a hands-on Data Engineer to own the onboarding of new client data and maintain reliable pipelines supporting direct mail, digital marketing, and reporting. The role involves understanding unfamiliar data feeds, building repeatable ingestion processes, and identifying issues before they result in bad data or missed campaign deliveries. It is a production-ownership, individual-contributor position, not a people-management role. The Data Engineer will work closely with the Senior Analytics Engineer to provide clean, consistent data for downstream analytics and reporting. The role includes setting up client and vendor feeds, mapping incoming data to established schemas, applying approved privacy and access rules, building validation and matching steps, and documenting each feed. The engineer will develop and maintain SQL, Python, and Spark pipelines; configure scheduling, file watchers, testing, and monitoring; and troubleshoot Databricks workflows, AWS jobs, and related tools. They will also take ownership of routine audience and mail-drop production within established approvals, including suppressions, address hygiene, count reconciliation, quality control, and vendor file preparation. The position is remote within the United States. The Dallas, Texas area is preferred but not required. Success includes learning the main feeds and drops, taking over daily checks, resolving production issues, setting up or improving client data flows, and eventually independently owning routine ingestion, pipeline maintenance, and standard mail drops.

Requirements

Several years of hands-on data engineering experience, ideally four or more, including production pipelines and failures personally diagnosed.
Ability to take a messy or poorly documented source file from initial inspection through to a reliable production data feed.
Strong attention to detail, including reconciling counts, questioning suspicious data, and implementing fixes that prevent recurrence.
Ability to work independently, explain technical issues clearly, and recognize when a business-rule or privacy question requires a decision from an owner.
Strong SQL and Python skills.
Practical experience with Spark or similar large-scale data processing.
Production cloud data engineering experience.
Experience with orchestration, monitoring, schema mapping, file-based integrations, access controls, Git, testing, and operational documentation.
Careful handling of personally identifiable information or protected health information in regulated data environments.

Nice To Haves

Experience with direct mail and/or digital marketing audience data, including deduplication, suppressions, CASS/NCOA, AccuZIP, AccuTrace, USPS tracking, or digital marketing sources and APIs.
Experience with healthcare, Medicare, or health plan marketing data.
Experience with SFTP, S3, Parquet, and client-facing reporting feeds.
Experience with Databricks and AWS; AWS-native or comparable experience is valued as the platform evolves.

Responsibilities

Set up new data feeds from client and vendor systems, including SFTP and scheduled file deliveries; identify source fields, changes, missing data, and inconsistent formats.
Map incoming data into established schemas and tables, identify sensitive fields, and apply approved privacy and access rules; escalate ambiguous data or policy questions.
Build validation and matching processes, including record counts, identity matching or LTID assignment, and CASS/NCOA processing where relevant.
Document each feed’s inputs, transformations, checks, outputs, and failure procedures.
Build and extend SQL, Python, and Spark pipelines for direct mail and digital clients using consistent data structures.
Set up scheduled jobs, file watchers, transformations, testing, and monitoring to minimize manual intervention.
Improve pipelines as source files, schemas, tools, and business requirements change.
Own day-to-day checks and troubleshooting for Databricks workflows, AWS jobs, and related tools; investigate failures and fix root causes.
Handle schema drift, bad source files, expired credentials, broken dependencies, stale data, and vendor-tool issues; improve alerts, recovery steps, and runbooks.
Manage routine audience and mail-drop production, including suppressions, address hygiene, count reconciliation, quality control, and vendor file preparation within established approvals.
Flag risks to scheduled client drops early, explain their impact, and recommend a safe resolution.
Hand off reliable, documented data to the Senior Analytics Engineer and collaborate on decisions about definitions or matching rules.
Partner with campaign, account, and technical colleagues to clarify requirements and resolve issues.