Data Engineer Client Data Operations

Limetree•Dallas, TX

4d•$115,000 - $145,000•Remote

About The Position

Limetree is hiring a hands-on Data Engineer to own the onboarding of new client data and keep data pipelines reliable for direct mail, digital marketing, and reporting work. This individual-contributor role involves interpreting unfamiliar client and vendor feeds, building repeatable ingestion processes, and identifying problems before they result in poor-quality data or missed campaign deliveries. The position works closely with the Senior Analytics Engineer and other campaign, account, and technical colleagues; it is not a people-management role. The Data Engineer will onboard client feeds, map data into established schemas, apply approved privacy and access rules, and build validation and matching steps. The role also builds and maintains SQL, Python, and Spark pipelines; sets up jobs, transformations, tests, and monitoring; and troubleshoots production workflows and related tools. Over time, the engineer will take ownership of routine audience and mail-drop production, including quality checks and vendor file preparation within established approvals. The role is remote within the United States, with the Dallas, Texas area preferred but not required. Limetree describes success as taking over daily checks and production troubleshooting during the first 90 days, then owning routine client ingestion, pipeline maintenance, and standard mail drops by six months. The base salary is $115,000–$145,000, depending on experience and qualifications.

Requirements

Several years of hands-on data engineering experience; ideally four or more years, including operating production pipelines and personally diagnosing failures.
Experience taking messy or poorly documented source files from initial inspection through delivery as reliable production data feeds.
Strong SQL and Python skills.
Practical experience with Spark or similar large-scale data processing.
Production cloud data engineering experience; Databricks and AWS are currently used, and AWS-native or comparable experience is valued.
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.
Strong attention to detail, including reconciling counts, questioning suspicious data, and making fixes that prevent recurring problems.
Ability to work independently, explain technical issues plainly, and identify when a business-rule or privacy decision needs an owner.

Nice To Haves

Experience with direct mail and/or digital marketing audience data.
Experience with deduplication, suppressions, CASS/NCOA, AccuZIP, AccuTrace, USPS tracking, digital marketing sources, or APIs.
Experience with healthcare, Medicare, or health plan marketing data.
Experience with SFTP, S3, Parquet, and client-facing reporting feeds.

Responsibilities

Set up new feeds from client and vendor systems, including SFTP and scheduled file deliveries; inspect 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; raise ambiguous data or policy questions before proceeding.
Build data validation and matching steps, including record counts, identity matching or LTID assignment, and CASS/NCOA processing where relevant.
Document feed inputs, transformations, checks, outputs, and failure procedures.
Build and extend SQL, Python, and Spark pipelines for new direct mail and digital clients using consistent data structures.
Set up scheduled jobs, file watchers, transformations, testing, and monitoring to minimize manual intervention.
Improve existing 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 address root causes.
Resolve schema drift, bad source files, expired credentials, broken dependencies, stale data, and vendor-tool issues; improve alerts, recovery steps, and runbooks.
Run 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 impacts, and recommend a safe resolution.
Hand off reliable, documented data to the Senior Analytics Engineer for downstream modeling, analysis, and reporting; collaborate on decisions about definitions and matching rules.
Partner with campaign, account, and technical colleagues to clarify requirements and resolve issues while keeping them informed about feeds and deliveries.