Mid Level Data Platform Engineer Jobs
Mid level data platform engineer jobs go to engineers ready to own pipeline architecture end to end, make infrastructure decisions with limited oversight, and guide junior teammates on data quality and tooling standards. Hiring runs across Technology & Software, Banking & Financial Services, and Retail, with 51% of openings remote or hybrid, and employers like RevSpring, Amazon, and TikTok USDS JV actively hiring at this level now.
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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 105+ Mid Level Data Platform Engineer Jobs
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Find JobsMid Level Data Platform Engineer Job Market
Who's Hiring
- RevSpring11
- Amazon5
- TikTok USDS JV5
- Figma3
- GEICO3
Top Industries Hiring
- Technology & Software43
- Banking & Financial Services7
- Retail6
- Investment & Asset Management6
- Insurance5
Mid Level Data Platform Engineer Jobs: Frequently Asked Questions
How do I get a mid level data platform engineer job?
Lead your applications with concrete ownership: pipelines you built from scratch, reliability improvements you drove, or data quality frameworks you introduced. Hiring managers at this level want to see that you made decisions independently, not just executed tasks. Tailor your resume to show the scope of each project, the tools you chose, and the measurable outcome you delivered.
Which companies hire mid level data platform engineers?
Companies hiring mid level data platform engineers right now include RevSpring, Amazon, and TikTok USDS JV, based on current listings on Migrate Mate as of July 2026. Hiring at this level comes from a wide range of employers, including high-growth tech companies, established enterprises modernizing their data infrastructure, and data-intensive businesses in finance, healthcare, and retail.
Are there remote mid level data platform engineer jobs?
Yes, and the share is significant. About 51% of mid level data platform engineer openings are remote or hybrid as of July 2026, reflecting how broadly distributed data platform teams have become. Search listings on Migrate Mate and filter by work setting to find openings that match your preference.
How do I move up to a mid level data platform engineer role?
The path from entry level to mid level is built on demonstrated ownership, not just time served. Focus on taking end-to-end responsibility for at least one production pipeline, deepening expertise in a core area like orchestration, streaming, or data modeling, and showing measurable impact such as reduced latency or improved data reliability. Employers look for engineers who can operate with less day-to-day guidance.
Which industries hire the most mid level data platform engineers?
Mid Level data platform engineer roles concentrate in Technology & Software, Banking & Financial Services, and Retail, based on current listings on Migrate Mate as of July 2026. These sectors share a dependence on scalable, reliable data infrastructure to power analytics, machine learning, and operational decision-making, which drives consistent demand for engineers at the mid level who can build and maintain those systems.