Mid Level AI Data Engineer Jobs
Mid level ai data engineer jobs go to professionals ready to own data pipelines end to end, make architectural decisions with limited oversight, and mentor junior teammates on best practices. Openings are 34% remote or hybrid across Technology & Software, Consulting & Professional Services, and Electronics & Hardware, with employers like Apple, Amazon Web Services, and JPMorganChase hiring at this level now.
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Showing 5 of 168+ Mid Level AI Data Engineer jobs
Project overview: The project involves migrating production data pipelines from legacy environments into a modern, cloud‑native data platform. The new platform enables domain‑oriented data products, scalable analytics, and embedded governance, with AI‑based tools supporting data quality, anomaly detection, privacy, and compliance.
- Position overview: We are looking for a Middle–Senior Software Engineer / Data Engineer with a strong background in data platform engineering and migration delivery. We are specifically seeking candidates with experience in platform migration initiatives, the ability to take ownership of an existing design, and the capability to deliver independently with minimal supervision.
- The ideal candidate will combine strong implementation skills with the ability to contribute to technical design discussions and execute complex migration work across modern cloud data ecosystems.
- The estimated salary for this position is up to USD 150,000 per year.
- Preference will be given to candidates who can work in a hybrid model from NYC, Iselin (NJ), or Charlotte (NC), three days per week.
Technology stack: Python, SQL, NoSQL, Databricks, Snowflake, Apache Airflow, Flask, Streamlit, LLM/RAG, Azure (AWS/GCP exposure is a plus).
- Responsibilities: Design, develop, and enhance scalable ETL and ELT data pipelines
- Take ownership of existing platform designs and deliver migration work independently with minimal guidance
- Contribute to solution design and ensure high-quality implementation aligned with platform architecture
- Lead and execute the migration of data assets and workloads to a modern cloud-based platform built on Databricks
- Build and maintain orchestration workflows using Apache Airflow or similar tools
- Optimize Snowflake data models for performance, scalability, and cost efficiency
- Work closely with product, data, and platform teams to support migration goals and platform evolution
- Contribute to cloud-native application design and deployment
- Collaborate with AI engineers to integrate AI/LLM-enabled capabilities where applicable
- Support the integration or development of MCP servers where relevant
- Requirements: Senior candidates: 10+ years of professional experience in software engineering, data engineering, or data platform development
- Mid-level candidates: 4+ years of professional experience in software engineering, data engineering, or data platform development
- Strong experience building production-grade data pipelines
- Hands-on experience with Databricks, Snowflake, and Azure
- Experience working on platform migration or modernization initiatives
- Proven ability to work from an existing design and deliver independently with minimal supervision
- Solid experience with Snowflake, including data modeling and performance tuning
- Hands-on experience with Apache Airflow or similar orchestration tools
- Strong Python development skills
- Advanced SQL skills and working knowledge of NoSQL databases
- Experience working within modern cloud data platforms and distributed engineering teams
- Good understanding of scalable architecture patterns and data platform best practices
- Nice to have: Experience with medallion architecture or similar layered data platform models
- Exposure to platform migration initiatives or large-scale enterprise data transformation programs
- Hands-on experience with large language models (LLMs)
- Experience with retrieval-augmented generation (RAG) patterns
- Experience with embeddings and vector databases
- Experience using Streamlit or similar tools to build GenAI interfaces
- Exposure to MCP server development or integration
Project overview: The project involves migrating production data pipelines from legacy environments into a modern, cloud‑native data platform. The new platform enables domain‑oriented data products, scalable analytics, and embedded governance, with AI‑based tools supporting data quality, anomaly detection, privacy, and compliance.
- Position overview: We are looking for a Middle–Senior Software Engineer / Data Engineer with a strong background in data platform engineering and migration delivery. We are specifically seeking candidates with experience in platform migration initiatives, the ability to take ownership of an existing design, and the capability to deliver independently with minimal supervision.
- The ideal candidate will combine strong implementation skills with the ability to contribute to technical design discussions and execute complex migration work across modern cloud data ecosystems.
- The estimated salary for this position is up to USD 150,000 per year.
- Preference will be given to candidates who can work in a hybrid model from NYC, Iselin (NJ), or Charlotte (NC), three days per week.
Technology stack: Python, SQL, NoSQL, Databricks, Snowflake, Apache Airflow, Flask, Streamlit, LLM/RAG, Azure (AWS/GCP exposure is a plus).
- Responsibilities: Design, develop, and enhance scalable ETL and ELT data pipelines
- Take ownership of existing platform designs and deliver migration work independently with minimal guidance
- Contribute to solution design and ensure high-quality implementation aligned with platform architecture
- Lead and execute the migration of data assets and workloads to a modern cloud-based platform built on Databricks
- Build and maintain orchestration workflows using Apache Airflow or similar tools
- Optimize Snowflake data models for performance, scalability, and cost efficiency
- Work closely with product, data, and platform teams to support migration goals and platform evolution
- Contribute to cloud-native application design and deployment
- Collaborate with AI engineers to integrate AI/LLM-enabled capabilities where applicable
- Support the integration or development of MCP servers where relevant
- Requirements: Senior candidates: 10+ years of professional experience in software engineering, data engineering, or data platform development
- Mid-level candidates: 4+ years of professional experience in software engineering, data engineering, or data platform development
- Strong experience building production-grade data pipelines
- Hands-on experience with Databricks, Snowflake, and Azure
- Experience working on platform migration or modernization initiatives
- Proven ability to work from an existing design and deliver independently with minimal supervision
- Solid experience with Snowflake, including data modeling and performance tuning
- Hands-on experience with Apache Airflow or similar orchestration tools
- Strong Python development skills
- Advanced SQL skills and working knowledge of NoSQL databases
- Experience working within modern cloud data platforms and distributed engineering teams
- Good understanding of scalable architecture patterns and data platform best practices
- Nice to have: Experience with medallion architecture or similar layered data platform models
- Exposure to platform migration initiatives or large-scale enterprise data transformation programs
- Hands-on experience with large language models (LLMs)
- Experience with retrieval-augmented generation (RAG) patterns
- Experience with embeddings and vector databases
- Experience using Streamlit or similar tools to build GenAI interfaces
- Exposure to MCP server development or integration
See All 168+ Mid Level AI Data Engineer Jobs
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Find JobsMid Level AI Data Engineer Job Market
Who's Hiring
- Apple8
- Amazon Web Services7
- JPMorganChase4
- Motional Ad4
- TikTok USDS JV3
Top Industries Hiring
- Technology & Software33
- Consulting & Professional Services22
- Electronics & Hardware13
- Banking & Financial Services11
- Insurance10
Mid Level AI Data Engineer Jobs: Frequently Asked Questions
How do I get a mid level ai data engineer job?
Position yourself around ownership, not just execution. Highlight projects where you designed or rebuilt a data pipeline, made a meaningful architectural call, or unblocked teammates. Tailor your resume to show scope, what you owned, what broke before you fixed it, and what improved because of your work. Concrete impact (scale, reliability, speed) consistently outperforms a list of tools.
Which companies hire mid level ai data engineers?
Companies hiring mid level ai data engineers right now include Apple, Amazon Web Services, and JPMorganChase, based on current listings on Migrate Mate as of July 2026. Mid level openings come from a mix of large technology firms running mature data platforms and growth-stage companies building AI infrastructure for the first time.
Are there remote mid level ai data engineer jobs?
Yes, remote and hybrid options are common at this level. About 34% of mid level ai data engineer openings are remote or hybrid as of July 2026, reflecting strong employer demand for experienced engineers who can work independently across distributed teams. On-site roles tend to cluster in major tech hubs where employers want closer collaboration on complex systems.
How do I move up to a mid level ai data engineer role?
The path from entry level to mid level comes down to deepening ownership over time. Build toward running a data project from requirements through production without close supervision. Develop fluency in orchestration, model serving infrastructure, or a specific data domain. Demonstrating measurable impact, like improving pipeline reliability or reducing processing costs, signals readiness for the responsibilities mid level roles carry.
Which industries hire the most mid level ai data engineers?
Mid Level ai data engineer roles concentrate in Technology & Software, Consulting & Professional Services, and Electronics & Hardware, based on current listings on Migrate Mate as of July 2026. These sectors drive hiring because they rely on large-scale data infrastructure and machine learning workflows that require engineers who can both build and maintain production-grade systems.