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Senior Data Engineer – Digital Marketing (Mexico)

AI Summary
This senior data engineering role builds and maintains scalable ETL pipelines for a digital-marketing media-mix modelling and scenario-planning product. The engineer will work extensively with PySpark and Databricks, including Auto Loader, complex transformations, performance tuning, and cloud cost optimization. Responsibilities include troubleshooting production pipeline bottlenecks and enforcing data quality, governance, and performance standards. The role requires close collaboration with engineering and business stakeholders while operating autonomously in a fully remote environment in Mexico.
Remote from
Mexico
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
8 Dec 2026
Listing views
31
Application actions
3
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Opportunity details

About this role.

About Truelogic

At Truelogic we are a leading provider of nearshore staff augmentation services headquartered in New York. For over two decades, we’ve been delivering top-tier technology solutions to companies of all sizes, from innovative startups to industry leaders, helping them achieve their digital transformation goals.

Our team of 600+ highly skilled tech professionals, based in Latin America, drives digital disruption by partnering with U.S. companies on their most impactful projects. Whether collaborating with Fortune 500 giants or scaling startups, we deliver results that make a difference.

By applying for this position, you’re taking the first step in joining a dynamic team that values your expertise and aspirations. We aim to align your skills with opportunities that foster exceptional career growth and success while contributing to transformative projects that shape the future.

Our Client

Our client is a challenger holding company built to transform marketing through a faster, cheaper, and more accessible media mix modelling (MMM) and scenario planning solution.

Job Summary

They are looking for a skilled Data Engineer with 5–7 years of experience to join their team. The ideal candidate will have hands-on expertise in ETL pipelines, data transformations, and performance optimization, with strong problem-solving skills and the ability to communicate effectively across technical and non-technical teams.

Responsibilities

  • Pipeline Architecture: Design, build, and maintain scalable ETL pipelines for large-scale data processing.

  • Core Tech Stack: Work extensively with PySpark and Databricks to execute complex data transformations and workflows.

  • Optimization & Efficiency: Fine-tune data pipelines to maximize performance, scalability, and cloud cost-efficiency.

  • Cross-Functional Collaboration: Partner with engineering and business teams to translate requirements into high-quality data solutions.

  • Troubleshooting & Maintenance: Debug, resolve, and prevent complex data processing and pipeline bottlenecks.

  • Data Standards: Enforce best practices for data quality, governance, and system performance.

Qualifications and Job Requirements

  • Experience: 5–7 years of hands-on data engineering experience building production-grade solutions.

  • Core Technologies: Strong proficiency in PySpark and extensive hands-on experience with Databricks.

  • Specialized Expertise: Advanced knowledge of Databricks AutoLoader, Python, best practices, performance tuning, and compute cost optimization.

  • Data Architecture: Deep understanding of end-to-end ETL processes, complex data transformations, and pipeline optimization.

  • Core Competencies: Exceptional problem-solving skills and the ability to communicate technical concepts clearly across both technical and business teams.

What We Offer

  • 100% Remote Work: Enjoy the freedom to work from the location that helps you thrive. All it takes is a laptop and a reliable internet connection.

  • Highly Competitive USD Pay: Earn an excellent, market-leading compensation in USD, that goes beyond typical market offerings.

  • Paid Time Off: We value your well-being. Our paid time off policies ensure you have the chance to unwind and recharge when needed.

  • Work with Autonomy: Enjoy the freedom to manage your time as long as the work gets done. Focus on results, not the clock.

  • Work with Top American Companies: Grow your expertise working on innovative, high-impact projects with Industry-Leading U.S. Companies.

Why You’ll Like Working Here

  • A Culture That Values You: We prioritize well-being and work-life balance, offering engagement activities and fostering dynamic teams to ensure you thrive both personally and professionally.

  • Diverse, Global Network: Connect with over 600 professionals in 25+ countries, expand your network, and collaborate with a multicultural team from Latin America.

  • Team Up with Skilled Professionals: Join forces with senior talent. All of our team members are seasoned experts, ensuring you’re working with the best in your field.

Apply now!

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe role requires 5–7 years of production data-engineering experience and advanced hands-on capability with PySpark, Databricks, ETL architecture, and performance optimization. Work involves resolving complex data-processing bottlenecks while balancing scalability, quality, and cloud costs.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$75,000
US market range (per year)$60k–$95k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No numeric employer compensation is disclosed. The annual cadence and USD market estimate are inferred from the senior full-time remote data-engineering scope in Mexico; these are AI estimates rather than verified employer pay.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How have you designed a scalable ETL pipeline in Databricks?

I start by defining data contracts, ingestion patterns, storage layers, and quality checks. I use incremental processing where possible, partition data appropriately, and build observable workflows with retries, monitoring, and clear failure handling.

What techniques do you use to optimize a slow PySpark job?

I inspect the Spark UI to identify skew, expensive shuffles, poor partitioning, and inefficient joins. I then reduce unnecessary transformations, select suitable join strategies, tune partition counts, cache only reusable datasets, and validate the impact with representative workloads.

When would you use Databricks Auto Loader?

I use Auto Loader for reliable, scalable ingestion of new cloud files when source volumes or file counts make manual file discovery inefficient. It supports incremental discovery, schema evolution controls, and integration with structured streaming and Delta Lake workflows.

How do you maintain data quality in production pipelines?

I implement validation at ingestion and transformation stages, including schema checks, null and uniqueness rules, referential checks, freshness thresholds, and reconciliation against source totals. Failed records are quarantined or routed for review, and quality metrics are monitored over time.

Describe how you would communicate a pipeline-cost optimization to a non-technical stakeholder.

I would explain the current cost driver in business terms, such as unnecessary processing volume or inefficient compute usage, then present the proposed change, expected savings, delivery risk, and any trade-offs. I would use a concise before-and-after estimate and agree on success metrics before rollout.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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