Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Senior Data Engineer – Digital Marketing (Brazil)
- Remote from
- Brazil
- Salary
- Undisclosed
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 8 Dec 2026
- Listing views
- 25
- Application actions
- 0
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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!
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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4/5Autonomy Level
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Sample interview questions
I would use a layered architecture, such as bronze, silver, and gold tables, with Auto Loader for incremental ingestion and Delta Lake for reliable storage. I would make transformations idempotent, partition data based on access patterns, implement data-quality checks, and orchestrate the workflow with Databricks Workflows or an equivalent scheduler.
I start with the Spark UI and query plans to identify skew, unnecessary shuffles, poor partitioning, or inefficient joins. I then optimize file sizes, partition strategy, join methods, caching only where justified, cluster sizing, and autoscaling settings, validating both runtime improvement and cost impact.
I use Auto Loader to incrementally ingest cloud files with schema inference or a controlled schema, schema evolution rules, checkpointing, and rescued data handling. I configure it to avoid repeated processing and connect downstream transformations to monitored Delta tables with clear operational alerts.
I define quality expectations for completeness, uniqueness, validity, timeliness, and referential consistency at each pipeline layer. Failed records are quarantined with observable error metrics, while lineage, access controls, table ownership, retention rules, and documentation support governance.
I would describe the business impact first, such as reporting latency, data completeness, or cost, and then explain the trade-offs in plain language. I would present practical options with expected timelines, risks, and recommendations so the stakeholder can make an informed decision.
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