Senior Data Engineer

Valtech

Lead complex data engineering initiatives across cloud environments and multiple stakeholders, defining ingestion, transformation, and serving patterns for analytics and AI. Coach and develop engineers, enforce best practices, and drive data quality, reliability, and readiness for downstream consumers.

Posted 7/6/2026full-timeRemote • 🇧🇷 BrazilSeniorWebsite

Tech Stack

Tools & technologies

CloudETLPySparkPythonSparkSQL

About the role

Key responsibilities & impact

Lead complex data engineering workstreams across multiple business areas, source systems, domains, or stakeholder groups.
Define data engineering approaches that align business needs, source-system realities, platform constraints, transformation patterns, and downstream consumption requirements.
Translate ambiguous stakeholder needs into structured pipeline strategies, ingestion patterns, transformation designs, curated data layers, and actionable recommendations.
Guide the design and implementation of scalable ingestion, transformation, and serving patterns across cloud warehouses, lakehouses, and analytical environments.
Establish and reinforce best practices for schema design, pipeline modularity, layered data architecture, data validation, lineage awareness, scheduling discipline, error handling, and maintainable engineering patterns.
Design and improve reusable data engineering workflows that support reporting, dashboarding, data science, AI workflows, and agentic use cases.
Support team staffing, work allocation, prioritization, and delivery quality across data engineering engagements.
Manage, coach, and develop practitioners through feedback, guidance, and performance support.
Review deliverables for clarity, technical rigor, quality, consistency, and business usefulness.
Help teams improve data quality, reduce pipeline fragility, strengthen source alignment, and increase trust in downstream governed datasets.
Support data engineering patterns that improve AI and agentic readiness, including metadata-rich datasets, retrieval-supportive organization, document and chunk preparation support, governed access paths, and scalable data availability for downstream workflows.
Collaborate with Analytics Engineers, Data Analysts, Data Scientists, AI Scientists, AI Engineers, AI Platform Engineers, and Architects to align data foundations with downstream analytical, AI, and platform needs.
Contribute to hiring, onboarding, capability development, and team maturity within the data engineering practice.
Follow established governance, privacy, security, and data-quality standards across the work of the team.

Requirements

What you’ll need

5+ years of experience in data engineering
Deep working knowledge of data engineering, cloud-based data platforms, and scalable pipeline design
Proven ability to define data engineering approaches in complex business environments with multiple systems, domains, and downstream consumers
Strong people leadership skills, including coaching, feedback, prioritization, and support for team development
Solid understanding of ETL and ELT concepts, schema evolution, orchestration, dependency management, layered data design, and downstream data consumption needs
Proficiency with SQL, Python, Spark, PySpark, and cloud-native data engineering workflows
Strong understanding of medallion, lakehouse, warehouse, or layered architectural patterns and their role in governed data reuse
Excellent analytical and problem-solving skills across data-quality issues, performance challenges, dependency risks, and transformation design
Practical understanding of how reliable data foundations enable machine learning, AI workflows, retrieval quality, agentic systems, and governed application behavior
Strong stakeholder management skills and the ability to communicate clearly with technical and non-technical audiences
Ability to balance delivery quality, team workload, business urgency, and stakeholder expectations across multiple workstreams
Strong written and verbal communication skills in English, with confidence in client-facing and leadership-facing settings
Ability to collaborate effectively across distributed teams in the Americas and across multiple disciplines
Upper-intermediate English level
Technical requirements: Microsoft Fabric, Microsoft Foundry, APIM Gateway, Application Insights, and Cosmos DB

Benefits

Comp & perks

Flexible work options, including remote and hybrid arrangements (country-dependent)
Career advancement opportunities, including international mobility and professional development programs
Learning and development resources, with access to cutting-edge tools, training, and industry experts

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Data EngineeringPipeline DesignETLELTSQLPythonSparkPySparkData QualityData Transformation

Soft Skills

CoachingFeedbackAnalytical SkillsProblem-SolvingCommunication