Data Engineer

Sparibis

Data Engineer building enterprise data lakes, pipelines, and cloud solutions for Sparibis, a professional solutions firm. Supporting Databricks, Palantir Foundry, Kafka, Spark, and AWS environments.

Posted 9/11/2026full-timeRemote • Florida • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data engineering, including the design and implementation of data architectures, ETL/ELT pipelines, and data integration processes. Proficient in utilizing tools such as Databricks, Spark, and AWS services to optimize data processing and ensure data quality.

Highest-signal resume keywords

Data Engineering ExpertiseETL/ELT Pipeline DevelopmentDatabricks ProficiencyApache Spark ExperienceAWS Services Integration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ArchitectureData ModelingData IntegrationETL/ELT ProcessesPython ProgrammingSQL ProficiencyApache SparkKafka StreamingData Quality ControlBatch and Streaming Data Processing

Soft Skills

Strong Communication SkillsTeam CollaborationProblem-Solving Ability

Tools & Technologies

DatabricksAWS S3AWS LambdaAWS KinesisDynamoDBPalantir FoundryGitJenkinsDockerCloudWatch

Certifications & Qualifications

CompTIA Security+CompTIA Network+

Industry Keywords

Data HubsData LakesData WarehousesData QualityData ManagementData AcquisitionData TransformationCloud-Based SolutionsData Lifecycle PoliciesEnterprise Data Engineering

Tech Stack

Tools & technologies

ApacheAWSCloudDockerDynamoDBETLJenkinsKafkaPySparkPythonSparkSQLSSIS

About the role

Key responsibilities & impact

Provide data engineering expertise in developing, implementing, integrating, and sustaining data architectures, data hubs, data lakes, and data warehouse solutions
Support design and development of data models, data structures, and data acquisition processes for the HC/HR Data Domain
Develop engineering and implementation plans for data hubs, acquisition, modeling, integration, and related data engineering activities
Research and evaluate enterprise data sources for authoritative and reliable data hub development
Plan, create, and maintain data architectures aligned with business requirements
Design, develop, maintain, and optimize ETL/ELT pipelines and data transformation processes
Develop and maintain batch and streaming pipelines using Spark, Python, Databricks, Palantir Foundry, Kafka, and related tools
Develop data acquisition processes to ingest, transform, validate, and integrate diverse enterprise data
Implement incremental loading, late-arriving data, processing-window, data-freshness, and lifecycle strategies
Automate manual processes and improve workflow efficiency, reliability, and scalability
Develop, maintain, and optimize Databricks and enterprise data-lake solutions
Configure, monitor, manage, and document Databricks clusters according to DON policies, standards, and security requirements
Develop and maintain Spark-based processing solutions using PySpark, Spark SQL, Data Frames, and Data Sets
Implement and maintain Delta Lake and Delta Live Tables solutions
Optimize data processing, storage, and query performance
Support Palantir Foundry ontologies, ETL/ELT pipelines, applications, user interfaces, and enterprise integrations
Develop and maintain Kafka streaming solutions, topics, Schema Registry, Kafka Streams, and ksqlDB components
Develop Python data-processing applications and AWS Lambda functions
Support data integration and processing with AWS S3, Kinesis, Lambda, and DynamoDB
Monitor, troubleshoot, and optimize cloud-based data-processing solutions
Develop data-quality controls, validation processes, and quality gates
Develop and execute data-driven and unit testing for Spark, Python, and other processing solutions
Establish data lifecycle policies covering retention, backup, recovery, and data management
Monitor and troubleshoot pipelines and processing jobs, resolving quality, performance, and integration issues
Improve data-processing performance, reliability, scalability, and maintainability

Requirements

What you’ll need

5+ years of professional data engineering-related experience
5+ years of IT experience focused on enterprise data engineering
Bachelor’s degree in computer science, Engineering, Mathematics, Statistics, or an IT-related field
Must be eligible to obtain and maintain a Secret security clearance
United States citizenship required for Secret security-clearance eligibility
Active CompTIA Security+ or CompTIA Network+ certification preferred; selected candidate must obtain CompTIA Security+ before supporting the program
Professional experience in data architecture, data engineering, data hubs, data lakes, and/or data warehouses
Experience designing, developing, implementing, and supporting enterprise-scale data engineering solutions
Experience with data modeling, data mapping, data quality, data integration, and data management
Experience with ETL/ELT pipelines and data transformation processes, including SSIS, Pentaho, or AWS Data Migration Service
Hands-on experience with Databricks required
Experience with Python and SQL
Experience with Apache Spark and/or PySpark, Spark SQL, Data Frames, and/or Data Sets preferred
Experience with batch and streaming data processing architectures
Experience with Kafka and/or other distributed streaming technologies, including Kafka Streams
Experience with AWS services such as S3, Lambda, Kinesis, and/or DynamoDB
Experience with Git-based version control, including branching, merging, pull requests, and repository management
Experience with CI/CD pipelines and cloud monitoring tools such as Jenkins, Docker, and/or CloudWatch preferred
Experience with Palantir Foundry preferred
Experience with the Jupiter data and analytics environment preferred but not required
Ability to debug, troubleshoot, design, and implement solutions to complex technical issues
Ability to thrive in a team-based environment
Experience briefing technology-solution benefits and constraints to partners, stakeholders, team members, and senior management
Strong written and verbal communication skills

Benefits

Comp & perks

Equal opportunity employer valuing diversity at all levels
Professional opportunity to work with enterprise data engineering, cloud, and analytics technologies