Data visualization Jobs in Dallas, TX

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.
• 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.

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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.
• Why Join Limetree You will own real production work, see the effect of getting data right, and improve the processes as you learn them.
• We need someone who likes building reliable systems and taking ownership of how they run.

Compensation Base salary:

• $115,000-$145,000, depending on experience and qualifications. Powered by JazzHR