Data Engineer

Job not on LinkedIn

🔥 0 minutes ago

🐊 Florida – Remote

🐊 Florida – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Sparibis

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2018

💼 Consulting

🔒 Cybersecurity

🏢 Enterprise

Consulting • Cybersecurity • Enterprise

Sparibis is an Orlando-based IT engineering and consulting firm. It provides program management and project controls support along with SDLC leadership and systems engineering. Its listed capabilities include cyber security engineering, cloud and DevSecOps engineering, mobility/software engineering, CRM and enterprise portals, test and operations engineering, and data management.

📋 Description

• 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

• 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

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