Data Engineer

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🕒 Setembro 11

🐊 Florida – Remoto

🐊 Florida – Remote

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🚰 Engenheiro de Dados

👻 Score fantasma 17%

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Sparibis

SiteLinkedInTodas as Vagas

11 - 50 funcionários

Fundada em 2018

💼 Consultoria

🔒 Cibersegurança

🏢 Corporativo

Consulting • Cybersecurity • Enterprise

A Sparibis é uma empresa de engenharia de TI e consultoria com sede em Orlando. Ela oferece suporte a gestão de programas e controle de projetos, juntamente com liderança em SDLC e engenharia de sistemas. Suas capacidades listadas incluem engenharia de segurança cibernética, engenharia de nuvem e DevSecOps, mobilidade/engenharia de software, CRM e portais empresariais, engenharia de testes e operações, e gerenciamento de dados.

Descrição

• 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

🎯 Requisitos

• 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

🏖️ Benefícios

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