Senior Data Engineer

Jones Lang LaSalle Americas, Inc.

Senior Data Engineer at JLL delivering data solutions in a global, collaborative environment. Developing and optimizing data pipelines, leveraging cloud and big data technologies.

Posted 7/2/2026full-timeRemote • 🇲🇽 MexicoSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced expertise in designing and implementing robust data pipelines using Databricks, Apache Spark, and BigQuery, with a strong focus on cloud-based environments. Proficient in data governance, ETL processes, and ensuring data quality and performance in multi-cloud settings.

Highest-signal resume keywords

Databricks ExpertiseData Pipeline DesignBig Data TechnologiesCloud Platforms (Azure, GCP)ETL Processes

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLPySparkSparkData WarehousingDimensional ModelingData GovernanceWorkflow AutomationObject-Oriented ProgrammingSoftware Design Patterns

Soft Skills

CollaborationProblem-SolvingCommunication

Tools & Technologies

DatabricksBigQueryAzureGCPAWS

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's Degree Preferred

Industry Keywords

Data EngineeringData QualityData ProcessingETLDevOpsTesting FrameworksSecurity Best Practices

Tech Stack

Tools & technologies

ApacheAWSAzureBigQueryCloudETLGoogle Cloud PlatformPySparkPythonSparkSQL

About the role

Key responsibilities & impact

Design and implement robust, scalable data pipelines using Databricks, Apache Spark, and Delta Lake as well as BigQuery
Design and implement efficient data pipeline frameworks, ensuring the smooth flow of data from various sources to data lakes, data warehouses, and analytical platforms
Troubleshoot and resolve issues related to data processing, data quality, and data pipeline performance
Document data infrastructure, data pipelines, and ETL processes, ensuring knowledge transfer and smooth handovers
Create automated tests and integrate them into testing frameworks
Configure and optimize Databricks workspaces, clusters, and job scheduling
Work in a Multi-cloud environment including Azure, GCP and AWS
Implement security best practices including access controls, encryption, and audit logging
Build integrations with market data vendors, trading systems, and risk management platforms
Establish monitoring and performance tuning for data pipeline health and efficiency
Collaborate with cross-functional teams to understand data requirements, identify potential data sources, and define data ingestion
Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver data solutions that meet their needs

Requirements

What you’ll need

Bachelor's degree in Computer Science, Data Engineering, or a related field (Master's degree preferred)
Minimum 5+ years of experience in data engineering or full-stack development, with a focus on cloud-based environments
Advanced expertise in managing big data technologies (Python, SQL, PySpark, Spark) with a proven track record of working on large-scale data projects
Strong Databricks experience
Advanced database/backend testing with the ability to write complex SQL queries for data validation and integrity
Strong experience in designing and implementing data pipelines, ETL processes, and workflow automation
Experience with data warehousing concepts, dimensional modeling, data governance best practices, and cloud-based data warehousing platforms (e.g., Google BigQuery, Snowflake)
Experience with cloud platforms such as Microsoft Azure, or Google Cloud Platform (GCP)
Experience working in DevOps model
Experience with Unit, Functional, Integration, User Acceptance, System, and Security testing of data pipelines
Proficiency in object-oriented programming and software design patterns
Familiarity with cutting-edge AI technologies and demonstrated ability to rapidly learn and adapt to emerging concepts and frameworks

Benefits

Comp & perks

Professional development opportunities
Flexible working arrangements