Data Engineer – DataOps, AWS

SysMap Solutions

. Develop, enhance, and maintain batch and streaming data pipelines.

Posted 9/30/2026full-timeRemote • BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Data Engineering with a focus on developing and maintaining data pipelines using Databricks, AWS, Apache Spark, and SQL. Proficient in applying DataOps practices to ensure data quality, integrity, and performance optimization.

Highest-signal resume keywords

Data EngineeringETL/ELT ProcessesDatabricksApache Spark / PySparkSQL

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentDataOps PracticesCI/CD ProcessesMonitoring and ObservabilityData Quality AssuranceLarge-Scale Data ArchitectureStructured and Semi-Structured Data ProcessingDelta Lake / Delta TablesWorkload Performance OptimizationData Integration

Soft Skills

Strong Communication SkillsProactive MindsetCollaborative ApproachOwnership and CommitmentCuriosity and Continuous Learning

Tools & Technologies

DatabricksAWS ServicesApache SparkPySparkSQL

Industry Keywords

Data Engineering StandardsData GovernanceProcess AutomationData ArchitectureData Integrity

Tech Stack

Tools & technologies

ApacheAWSCloudETLPySparkPythonSparkSQL

About the role

Key responsibilities & impact

Develop, enhance, and maintain batch and streaming data pipelines.
Design and implement data engineering solutions using Databricks and AWS services.
Apply DataOps practices focused on process automation, standardization, quality, and reliability.
Develop and maintain CI/CD processes for pipelines, notebooks, jobs, and data components.
Implement monitoring, observability, logging, and alerting mechanisms.
Ensure data quality, integrity, availability, and traceability.
Work with the processing and transformation of large volumes of data.
Develop solutions using Apache Spark / PySpark and SQL.
Integrate different data sources and systems.
Contribute to the definition and evolution of data architecture.
Support initiatives to optimize infrastructure and cloud performance and costs.
Identify and resolve performance issues, pipeline failures, and data inconsistencies.
Collaborate with Data Analysts, Data Scientists, Architects, Engineering teams, and business stakeholders.
Contribute to the definition of data engineering standards, best practices, and governance.

Requirements

What you’ll need

Solid experience in Data Engineering.
Experience developing ETL/ELT processes and data pipelines.
Advanced knowledge of SQL.
Experience with Python and/or PySpark.
Knowledge of large-scale data architecture and processing.
Experience working with structured and semi-structured data.
Hands-on experience with Databricks.
Knowledge of Apache Spark / PySpark.
Experience with Delta Lake / Delta Tables.
Knowledge of Databricks Jobs, Workflows, and processing mechanisms.
Knowledge of workload performance best practices and optimization techniques.
Strong sense of ownership and commitment to delivery.
Proactive and collaborative mindset.
Ability to take on challenges and see them through to completion.
Strong communication skills and ability to work effectively with multidisciplinary teams.
Curiosity and a continuous desire to learn and grow.
Mindset focused on building and continuous improvement.

Benefits

Comp & perks

No benefits, perks, or additional compensation are explicitly stated in the posting.