Remote Lead Data Engineer Jobs
Remote lead data engineer jobs are in active demand across the U.S. at remote-first companies and distributed engineering teams in technology, financial services, healthcare, and beyond. Employers hiring remote lead data engineers right now include First Citizens Bank, Trane Technologies, and JLL. Scan the live roles below and apply to whichever ones fit.
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What is special about Lighthouse?
Lighthouse is built on a foundation of unique, compassionate, highly driven individuals. We elevate the strengths and talents of those around us while leveraging opportunities for growth. We offer the experience of solving complex problems while continuing to grow multiple facets of your career. Lighthouse is where innovation meets support and where collaboration is the key ingredient to success. We grow together and are stronger together.
What’s unique about this role?
The Senior Lead Data Engineer on the Prism team sets the technical direction for designs and delivers the core data engineering infrastructure for Lighthouse's eDiscovery AI products — including AI-enabled TAS (Technology Assisted Solutions) and Generative AI document analysis services. This engineer is the senior technical authority and hands-on lead for data engineering, owning the architecture of highly scalable, reliable, and fault-tolerant data pipelines for multi-million document corpora, operationalizing ML model workflows, and guiding the design of data systems that support the full eDiscovery AI lifecycle: from ESI ingestion and text extraction through encoder model training, document scoring, and LLM-based RAG pipelines. The role partners closely with data science, product, and engineering leaders and raises the technical bar across the team through architecture leadership, standards, and mentoring.
***This is a part-time role - 24 hours per week***
What will this person do?
- Set the technical direction and own the end-to-end architecture for data engineering across Prism eDiscovery AI products, ensuring designs are scalable, reliable, secure, and cost-effective.
- Lead the design, build, and management of data preprocessing, validation, and transformation pipelines that extract metadata and full text from heterogeneous ESI corpora at multi-million document scale.
- Lead the build and operation of Data Orchestration platforms that coordinate training data preparation, model scoring, embedding generation, and result delivery across eDiscovery AI workflows.
- Architect and implement scalable, performant data pipelines that cleanse, structure, and integrate disparate ESI datasets into optimal formats for encoder model training (BERT fine-tuning) and LLM context preparation.
- Direct the design and evolution of infrastructure for LLM/Generative AI applications: document chunking, embedding generation pipelines, vector database population, and retrieval layer optimization for RAG architectures.
- Define and drive data engineering standards, best practices, and design patterns, including code quality, testing, documentation, and design reviews.
- Establish the approach and tooling to monitor, debug, and analyze data pipeline health, data quality, and model serving data flows in production, and lead resolution of complex production issues.
- Design and govern portable, scalable data schemas for document metadata, model inputs/outputs, scoring results, and embeddings.
- Lead experiments to validate efficiency, throughput, and cost of modern data engineering stacks for legal AI workloads and translate findings into roadmap recommendations.
- Own technical recommendations regarding buy vs. build decisions for data infrastructure components and present them to engineering and product leadership.
- Partner with data science, product, and engineering stakeholders to shape requirements, plan, and sequence technical work, and align data engineering priorities with business goals.
- Mentor and provide technical guidance to data engineers, fostering skill growth and consistent engineering practices across the team.
- Perform other related duties as assigned.
Bring your passion and together we will shine. It would also be great if you had the following:
- Degree in computer science or computer engineering with 8+ years of experience in a data engineering related field, including experience leading the technical design of large-scale data platforms (required).
- Demonstrated technical leadership: setting architecture direction, leading design reviews, and guiding engineers on complex, cross-team initiatives.
- Expertise building data pipelines for unstructured document data: text extraction, OCR processing, metadata extraction, and legal document format handling.
- Experience implementing big data workflows in cloud-native tech stacks (Azure preferred): Spark, Databricks, Airflow, Azure Data Factory, Kafka, and Kubernetes.
- Deep knowledge of data science and ML pipeline patterns: training data preparation, feature engineering, embedding generation, and MLOps operationalization.
- Experience with vector databases and semantic search infrastructure (Azure AI Search, Pinecone, or similar) for RAG pipeline support.
- Experience with relational and NoSQL databases, cloud-native storage technologies, and data serialization formats (Parquet, Delta Lake, JSON).
- Experience working with data scientists to operationalize encoder-based classification models and LLM applications in production.
- Strong communication skills, with the ability to explain complex technical trade-offs to technical and non-technical stakeholders.
- Proficiency with agile development methodologies.
Work Environment and Physical Demands
- Duties are performed in a typical office environment while at a desk or computer table.
- Duties require the ability to use a computer, communicate over the telephone, and read printed material, in a quiet and professional setting.
- Duties may require being on call periodically and working outside normal working hours (evenings and weekends).
Lighthouse celebrates and thrives on diversity and is an Equal Opportunity Employer. We hire, train, and promote regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. We welcome any talents and contributions you can bring to the team and are deeply committed to growing an environment where everyone can feel safe, is respected, and can show up as themselves. Come as you are!
As required by applicable pay transparency laws, Lighthouse complies with compensation disclosure requirements for roles that may be hired in locations under these requirements. Factors that may be used to determine your actual salary may include a wide array of factors, including: your specific skills and experience, geographic location, or other relevant factors. The salary range for this position may be tailored to be lower or higher in different talent markets.
This role will be eligible to participate in an annual bonus or incentive program.
As a trailblazer and catalyst for change, Lighthouse rises to each opportunity to help our clients, and our people do what they do best—shine.
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Find JobsRemote Lead Data Engineer Job Market
Who's Hiring
Top Industries Hiring
- Manufacturing
- Insurance
What Employers Look For
The qualifications that appear most often in remote lead data engineer jobs.
- Five or more years of experience designing and building large-scale data pipelines
- Proficiency in SQL and at least one of Python, Scala, or Java for data engineering
- Hands-on experience with a major cloud data platform such as AWS, GCP, or Azure
- Experience with distributed processing frameworks such as Apache Spark or Apache Flink
- Familiarity with workflow orchestration tools such as Apache Airflow or Prefect
- Experience with data warehousing solutions such as Snowflake, BigQuery, or Redshift
Tips for Your Remote Lead Data Engineer Job Search
Apply early to remote roles that fit
Migrate Mate lists remote lead data engineer openings from across the U.S. in one place, so you can find roles that match your stack and seniority level and apply directly without sorting through location-filtered results.
Show async leadership in your portfolio
Remote hiring managers want proof you can lead data work without in-person coordination. Document architecture decisions, write up pipeline design choices in a public repo or personal site, and link to any async technical writing you've done for a team.
Signal remote readiness in your cover letter
Mention the tools you use to stay aligned on distributed teams, such as dbt docs, Confluence, Notion, or Slack-based code review workflows. Remote employers specifically screen for candidates who default to written communication over verbal check-ins.
Prepare for remote-first technical screens
Many distributed teams run take-home assessments rather than live coding rounds. Practice writing clear, well-commented SQL and Python in a shared environment, and get comfortable explaining architectural tradeoffs in writing as well as on video calls.
Remote Lead Data Engineer Jobs: Frequently Asked Questions
How do I get a remote lead data engineer job?
Remote employers hiring lead data engineers screen hard for self-direction, clear async written communication, and the ability to lead distributed teams without in-person oversight. Strong candidates demonstrate hands-on work with modern data stack tools such as dbt, Spark, Airflow, and cloud data platforms, plus a history of owning pipelines end-to-end. A public portfolio, open-source contributions, or documented architecture decisions give remote hiring managers the evidence they need.
Which companies hire remote lead data engineers?
Employers currently hiring remote lead data engineers include First Citizens Bank, Trane Technologies, and JLL, per current remote listings on Migrate Mate as of November 2026. Remote-first software companies, fintech firms, and distributed healthcare technology teams make up the largest share of open roles for this position.
Can you get a remote lead data engineer job with no experience?
Yes, but remote lead data engineer roles are harder to land without experience because distributed teams expect you to operate independently from day one. Remote-first startups and early-stage companies are more likely to consider candidates building their experience, especially those who can show self-directed projects, contributed pipelines on GitHub, or freelance data work that demonstrates you can deliver without close supervision.
Do you need a degree for remote lead data engineer jobs?
Not always. Remote employers in this field increasingly weigh demonstrated technical skills, portfolio work, and real pipeline ownership over formal credentials. A strong GitHub profile, cloud data platform certifications, and verifiable contributions to production data systems can carry significant weight, particularly at remote-first companies that evaluate candidates through technical screens rather than credential filters.
Which industries hire the most remote lead data engineers?
Most remote lead data engineer openings sit in Manufacturing and Insurance, per current remote listings on Migrate Mate as of November 2026. These sectors rely on distributed engineering teams and generate the data volume that makes a dedicated lead engineer role necessary.
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