Data Engineer Client Data Operations
- Salary
- $115K–$145K
- Hiring from
- United States
- Work type
- Remote
- Posted
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An important note before applying: We're a small, growing team, and openings like this can attract 1,000 or more applications. We want to give as many qualified candidates as possible a fair shot, but there's only so much a small team can do when reviewing that volume while also doing the work that's the reason this opportunity exists.
Data Engineer Client Data Operations
Location: Remote (United States; Dallas, TX area preferred, not required)
We specialize in industries where customer interaction is high-value and high-stakes, including healthcare, financial services, and other regulated or complex environments. Our work connects insight to action, using behavioral science and data to inform strategy, creative, and delivery in ways that drive measurable outcomes for our clients.
Limetree has been recognized multiple times on the Inc. 5000 as well as a national Inc. Best Workplace, and holds multiple patents that inform our approach to marketing performance, decision-making, and execution.
This is a production ownership role. You will be the first person working through data and pipeline failures and, as you learn our processes, take over the technical execution of routine mail drops. You will work closely with our Senior Analytics Engineer, who depends on clean, consistent data for downstream analytics and reporting. This is an individual contributor role, not a people-management position.
• 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 moving forward.
• Build the validation and matching steps that make data usable, including record counts, identity matching or LTID assignment, and CASS/NCOA processing where relevant.
• Document each feed so someone else can understand the inputs, transformations, checks, outputs, and what to do when it fails.
• Set up scheduled jobs, file watchers, transformations, testing, and monitoring so new data streams run with minimal manual intervention.
• Improve existing pipelines as source files, schemas, tools, and business requirements change.
• Handle schema drift, bad source files, expired credentials, broken dependencies, stale data, and vendor-tool issues; improve alerts, recovery steps, and runbooks.
• Take ownership of routine audience and mail-drop production, including suppressions, address hygiene, count reconciliation, QC, and vendor file preparation, within established approvals.
• Flag risks to scheduled client drops early, explain what is affected, and recommend a safe path to resolution.
• Partner with campaign, account, and technical colleagues to clarify requirements and resolve issues without making them chase the status of a feed or delivery.
• By six months: Own routine new-client ingestion and pipeline maintenance, be the first responder for issues, and run standard mail drops with documented quality and release checks.
• The result: New client data is repeatable and trustworthy; pipeline failures have a clear owner; routine troubleshooting and drop execution no longer depend on a single senior colleague.
• Comfort taking a messy or poorly documented source file from first inspection through a reliable production data feed.
• Strong attention to detail: you reconcile counts, question suspicious data, and make fixes that prevent a problem from coming back.
• Able to work independently, explain technical issues plainly, and know when a business rule or privacy question needs an owner to decide.
• Production cloud data engineering experience. We currently use Databricks and AWS, but value strong AWS-native or comparable experience as our platform evolves.
• Experience with orchestration, monitoring, schema mapping, file-based integrations, access controls, Git, testing, and operational documentation.
• Careful handling of personally identifiable or protected health information in regulated data environments.
• Healthcare, Medicare, or health plan marketing data; SFTP, S3, Parquet, and client-facing reporting feeds.
To help, we're using an AI-powered verbal screening tool where you can talk through your experience and how you approach problems. We know... we know... another AI tool in the hiring process. We're not exactly fans of that either, but we believe it gives more people an opportunity to show us what they know beyond what's on their résumé.
We're not looking for perfect or rehearsed answers. In fact, we'd much rather hear a genuine, imperfect answer than something polished that doesn't really tell us anything. We want to hear about your actual experience, and our team will make the hiring decisions, not the AI.
Please complete the screening questions as part of your application. Quick Apply or Easy Apply submissions without completed responses may not be considered.
Data Engineer Client Data Operations
Location: Remote (United States; Dallas, TX area preferred, not required)
About Limetree
Limetree is a performance-focused marketing agency that blends behavioral science, data, and practical marketing know-how to help organizations say the right thing, to the right people, in ways that actually work.We specialize in industries where customer interaction is high-value and high-stakes, including healthcare, financial services, and other regulated or complex environments. Our work connects insight to action, using behavioral science and data to inform strategy, creative, and delivery in ways that drive measurable outcomes for our clients.
Limetree has been recognized multiple times on the Inc. 5000 as well as a national Inc. Best Workplace, and holds multiple patents that inform our approach to marketing performance, decision-making, and execution.
Role Summary
We are hiring a hands-on Data Engineer to own how new client data gets into our systems and keep the pipelines behind our direct mail, digital marketing, and reporting work reliable. You will take unfamiliar feeds, make sense of them, build repeatable ingestion processes, and catch problems before they become bad data or missed campaign deliveries.This is a production ownership role. You will be the first person working through data and pipeline failures and, as you learn our processes, take over the technical execution of routine mail drops. You will work closely with our Senior Analytics Engineer, who depends on clean, consistent data for downstream analytics and reporting. This is an individual contributor role, not a people-management position.
What You Will Do
Onboard Client Data
• Set up new 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. Raise ambiguous data or policy questions before moving forward.
• Build the validation and matching steps that make data usable, including record counts, identity matching or LTID assignment, and CASS/NCOA processing where relevant.
• Document each feed so someone else can understand the inputs, transformations, checks, outputs, and what to do when it fails.
Build New Pipelines
• Build and extend SQL, Python, and Spark pipelines as we bring on new direct mail and digital clients, using consistent data structures rather than one-off solutions.• Set up scheduled jobs, file watchers, transformations, testing, and monitoring so new data streams run with minimal manual intervention.
• Improve existing pipelines as source files, schemas, tools, and business requirements change.
Keep Production and Campaign Delivery Running
• Own the day-to-day checks and troubleshooting for current 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.
• Take ownership of routine audience and mail-drop production, including suppressions, address hygiene, count reconciliation, QC, and vendor file preparation, within established approvals.
• Flag risks to scheduled client drops early, explain what is affected, and recommend a safe path to resolution.
Work With the Team
• Hand off reliable, documented data to our Senior Analytics Engineer for downstream modeling, analysis, and reporting; work together when definitions or matching rules need decisions.• Partner with campaign, account, and technical colleagues to clarify requirements and resolve issues without making them chase the status of a feed or delivery.
What Success Looks Like
• First 90 days: Understand the main feeds and drops, take over daily checks, fix production issues with support, and independently set up or improve a client data flow with review.• By six months: Own routine new-client ingestion and pipeline maintenance, be the first responder for issues, and run standard mail drops with documented quality and release checks.
• The result: New client data is repeatable and trustworthy; pipeline failures have a clear owner; routine troubleshooting and drop execution no longer depend on a single senior colleague.
What We Are Looking For
Experience and Judgment
• Several years of hands-on data engineering experience, ideally four or more, including pipelines you have operated in production and failures you personally diagnosed.• Comfort taking a messy or poorly documented source file from first inspection through a reliable production data feed.
• Strong attention to detail: you reconcile counts, question suspicious data, and make fixes that prevent a problem from coming back.
• Able to work independently, explain technical issues plainly, and know when a business rule or privacy question needs an owner to decide.
Technical Skills
• Strong SQL and Python; practical experience with Spark or similar large-scale data processing.• Production cloud data engineering experience. We currently use Databricks and AWS, but value strong AWS-native or comparable experience as our platform evolves.
• Experience with orchestration, monitoring, schema mapping, file-based integrations, access controls, Git, testing, and operational documentation.
• Careful handling of personally identifiable or protected health information in regulated data environments.
Helpful Background
• Direct mail and/or digital marketing audience data, including deduplication, suppressions, CASS/NCOA, AccuZIP, AccuTrace, USPS tracking, or digital marketing sources and APIs.• Healthcare, Medicare, or health plan marketing data; SFTP, S3, Parquet, and client-facing reporting feeds.
