> Markdown version of [/jobs/ext/2935069-data-engineer](https://www.wearedevelopers.com/jobs/ext/2935069-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Engineer

- **Company:** Collabera
- **Location:** Houston, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $135,000.0 - $165,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Encodings, Data Architecture, Data Validation, Data Cleansing, Information Engineering, Data Governance, Data Integration, Data Integrity, Data Systems, Database Queries, Document-Oriented Databases, Fault Tolerance, Python (Programming Language), Machine Learning, Regression Testing, Mobile Analytics, DataOps, Search Technologies, SQL Databases, Systems Integration, Data Server Interface, Enterprise Software Applications, Data Classification, Retrieval-Augmented Generation, Large Language Models, Usage Tracking, Pyspark, Data Management, Network Server, Data Pipelines, Databricks
- **Published:** September 16, 2026
- **Apply:** https://www.collabera.com/submit-resume/

## About the Role

o 5+ years of experience in Data Engineering, working with data from multiple enterprise source systems.
o Strong hands-on experience with Databricks.
o Strong Python and PySpark development experience.
o Strong SQL skills.
o 2-3+ years of hands-on experience with MCP Servers / Model Context Protocol.
o Hands-on experience building and implementing RAG models/architectures.
o Experience connecting to, querying, and integrating data through MCP servers.
o Strong experience building self-healing or fault-tolerant data pipelines and error recovery workflows.
o Experience with schema validation and data quality frameworks.
o Strong understanding of Bronze/Silver/Gold data architecture.
o Experience with LLM integration patterns, AI agents, tool-use frameworks, and AI-enabled data solutions.
o Experience with vector databases such as Pinecone, Weaviate, or equivalent.
o Experience building data pipelines and infrastructure suitable for AI/ML workloads.
o Understanding of data governance, lineage, monitoring, observability, and data quality.

## Description

o Design, build, and maintain scalable, self-healing data pipelines using Databricks, Python, PySpark, and SQL.
o Develop data pipelines across Bronze, Silver, and Gold/Certified Gold layers, ensuring data quality and reliability.
o Ingest, transform, and serve data from dozens of source systems, including enterprise applications, financial systems, IoT, web/mobile analytics, and third-party platforms.
o Build error recovery and quarantine workflows to isolate failed records while allowing valid data to continue through the pipeline.
o Implement schema validation, data quality checks, anomaly detection, automated testing, regression testing, and data observability.
o Develop and maintain data models with proper data grain, keys, referential integrity, lineage, and governance.
o Build infrastructure that supports AI/ML workloads, including feature stores, embedding pipelines, vector search, and real-time serving layers.
o Build and maintain MCP server integrations that expose enterprise data to LLM-powered tools and AI agents.
o Develop integrations that allow AI applications to connect to, query, and retrieve data through MCP servers.
o Design and implement RAG (Retrieval-Augmented Generation) architectures and integrate LLM-powered solutions with enterprise data.
o Work with vector databases such as Pinecone, Weaviate, or similar technologies.
o Support AI model training, evaluation, deployment, monitoring, and productionization in partnership with Data Science and Product teams.
o Evaluate AI-powered data engineering and data quality tools, including solutions for automated schema detection, cataloging, completeness checks, and data validation.
o Use AI-assisted testing and evaluation approaches to validate data and AI outputs against business requirements.
o Develop APIs and data interfaces that enable AI products and internal applications to query and interact with data in real time.
o Implement data governance practices covering access controls, PII handling, data classification, compliance, and appropriate AI data usage.
o Build monitoring, alerting, SLA tracking, and data freshness capabilities into data platforms.
o Document data models, pipeline architectures, AI integrations, and reusable engineering patterns.


## Related Videos

- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [A Brief History of Data Storage](https://www.wearedevelopers.com/videos/974-a-brief-history-of-data-storage)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning)
- [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology)

## Related Articles

- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)