Matlen Silver

Sr. Data Engineer- Charlotte, NC

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Sr. Data Engineer in Charlotte, NC, on an 8-9 month contract, offering a hybrid schedule. Key requirements include 5+ years in Data Engineering, SQL, and big data technologies, with 3+ years on Google Cloud Platform.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
April 21, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
Hybrid
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
Charlotte, NC
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🧠 - Skills detailed
#Data Governance #GCP (Google Cloud Platform) #Cloud #Observability #Load Balancing #Deployment #Integration Testing #Programming #Security #"ETL (Extract #Transform #Load)" #Data Architecture #Data Framework #Data Engineering #UAT (User Acceptance Testing) #Model Deployment #Scripting #Unit Testing #Big Data #Data Science #Computer Science #SQL (Structured Query Language) #Data Quality #BI (Business Intelligence) #Scala
Role description
Job Title: Data Engineer Location: Charlotte NC 28203 Schedule: Hybrid (3 days on-site and 2 days remote) Position: Contract Duration: 8-9 month Interview Mode: In person Employment Type: W2 Job Description: Job Overview: We are seeking a Data Engineer / Data Solutions Engineer to deliver end-to-end data solutions for simple to moderately complex business challenges. This role focuses on translating business needs into technical requirements, designing scalable architectures, and supporting the implementation and maintenance of robust data solutions. You will act as a technical expert, helping evaluate the right platforms and technologies while ensuring best practices are followed across reliability, scalability, performance, security, and long-term maintainability. Key Responsibilities: • Deliver data solutions across multiple applications, resolving moderately complex business problems with limited guidance • Translate business requirements into technical specifications and scalable solution designs • Participate in planning, estimation, and mentoring junior engineers • Evaluate build vs. buy decisions and recommend best-fit platforms and technology stacks • Lead the design and development of scalable, flexible, and maintainable data and enterprise solutions • Ensure adherence to architectural standards and best practices • Develop and enforce testing strategies, including unit testing, integration testing, and support for user acceptance testing • Define and implement data solutions aligned with business objectives, SLAs, data quality, and observability standards • Collaborate with data governance teams on data retention, privacy, and security standards • Partner with data science teams to support model deployment and scalability • Analyze and structure data to support business insights and decision-making • Design and build operational and analytical (including self-service) data applications • Contribute to infrastructure initiatives including design, cost-benefit analysis, implementation, and support • Evaluate design alternatives and manage trade-offs across risk, cost, and performance • Work with networking and cloud concepts such as VPCs, subnetting, routing, security groups, and load balancing Minimum Qualifications: • Bachelor’s Degree in Engineering, Computer Science, Computer Information Systems (CIS), or related field (or equivalent work experience) • 5+ years of experience in Data or BI Engineering, Data Warehousing/ETL, or Software Engineering • 4+ years of experience working on projects involving the implementation of solutions using Software Development Life Cycle (SDLC) methodologies • 5+ years of experience in object-oriented or structured programming, SQL, and scripting • 5+ years of experience with big data technologies • 3+ years of experience with cloud-based big data platforms, preferably Google Cloud Platform (GCP) Preferred Skills: • Strong understanding of data architecture and distributed systems • Experience building scalable and reusable data frameworks • Knowledge of data governance, data quality, and observability best practices • Ability to collaborate across cross-functional teams including engineering, data science, and business stakeholders • Strong analytical thinking and problem-solving skills