Data Engineer

🕒 August 10

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 46%

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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v4c.ai

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🏥 Healthcare

🏭 Manufacturing

Consulting • Healthcare • Manufacturing

v4c. ai is a Databricks-focused data and AI services firm that helps enterprises modernize data platforms and accelerate AI initiatives. They provide advisory, migration, MLOps, generative AI, and data governance services, plus prebuilt migration accelerators and production-ready ML pipelines to reduce time-to-value. v4c. ai serves regulated and data-intensive industries (financial services, retail, manufacturing, healthcare) and emphasizes Databricks implementation, scalable model deployment, and long-term operational support.

📋 Description

• Design, develop, refine, and implement high-quality data architecture solutions for large-scale, complex data platforms • Establish best practices for modular, reusable, maintainable, and scalable data engineering code • Develop and maintain greenfield data engineering projects and production-level data pipelines • Design data-driven solutions for complex cybersecurity challenges • Architect end-to-end data platforms by evaluating technical approaches, trade-offs, risks, and performance requirements • Work hands-on with engineers to implement data architecture and pipeline solutions • Collaborate with DevOps and MLOps teams to monitor and optimize data pipelines and machine learning workflows • Improve model and pipeline performance through monitoring, troubleshooting, and tuning to meet latency and cost requirements • Evaluate third-party AI tools against internally developed solutions to maximize business value and ROI • Establish data governance, data quality, and security standards for ethical and compliant use of sensitive information in AI models • Develop incremental development strategies and production release plans aligned with long-term product vision • Serve as the data engineering subject matter expert for the AI team • Ensure data pipelines are reliable, scalable, secure, and optimized for production workloads

🎯 Requirements

• Bachelor’s degree in Engineering, Computer Science, or a related technical field • At least 4 years of experience working with data platform solutions across data and analytics throughout the software development life cycle (SDLC) • At least 2 years of experience designing and mapping requirements into Databricks data pipeline capabilities on Google Cloud Platform (GCP) • Hands-on experience with Databricks Lakehouse architecture, including Databricks Datalake and Data Catalog capabilities • Experience designing Databricks Medallion Architecture, including data structure, data access, and data quality decisions • Strong experience with batch, mini-batch, and real-time data processing • Hands-on experience with ELT/ETL processes, data pipelines, and orchestration tools • Experience processing large volumes of unlabeled data with complex schemas • Strong understanding of data modeling techniques and modern data platform trends, including AI applications in data engineering • Proficiency in Python, Pandas, PySpark, and related data processing frameworks • Experience with AI and machine learning tools such as TensorFlow, PyTorch, Keras, LangChain, LangSmith, and Infrastructure as Code (IaC) • Experience working with structured and unstructured data formats, including Parquet, PostgreSQL, DuckDB, JSON, and PDF • Experience with infrastructure sizing and estimation for data platform solutions • Strong experience as an individual contributor with a hands-on engineering mindset and delivery focus • Advanced degree in Engineering, Computer Science, Machine Learning, or Artificial Intelligence is preferred • Preferred industry experience in cybersecurity, automotive, energy, or healthcare • Preferred experience in both large enterprises and small companies • Familiarity with supervised and unsupervised machine learning models • Experience with AI-driven data engineering solutions and integration of AI capabilities into data platforms • Knowledge of data governance, security, and compliance best practices • Excellent verbal and written communication skills • Strong collaboration, analytical thinking, problem-solving, self-management, prioritization, ownership, initiative, and independent-working abilities