ETL Data Engineer | Onsite
At a glance
Mid-level Data Engineering role at Fa Ertb Saasfaprod1. United States · $42,000–$147,000 base.
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain batch and streaming pipelines that feed analytics, machine learning and operational reporting
- Use Python and SQL to bring data in, transform it and connect systems, including healthcare claims and clinical documents
- Create REST and GraphQL interfaces so microservices can access data efficiently
- Automate pipeline releases and connect ML models to production workflows with data science and MLOps teams
- Track data quality and system behavior, logging model outputs, confidence levels and fallback events
What you bring
- At least five years building data pipelines or backend data workflows with Python, Java or a similar language
- At least two years designing REST or GraphQL data services, plus production experience integrating ML or AI models
- Experience with structured and unstructured data, including healthcare claims or clinical text, and the end-to-end ML lifecycle
- At least two years with cloud platforms, streaming systems, relational databases and CI/CD tools
Who this fits
This mid-level engineering role suits someone who has built production data pipelines and worked across cloud, streaming, API and ML systems. The position is onsite, and independent contractors may apply. A computer science, data engineering or information systems degree, or equivalent experience, is accepted.
From the employer
Responsibilities
- Design, build, and maintain scalable data pipelines to support analytics, ML, and operational reporting.
- Develop robust data ingestion, transformation, and integration workflows using Python, SQL, and modern data engineering frameworks.
- Build and maintain batch and streaming data pipelines leveraging technologies such as Kafka (or similar pub/sub tools).
- Work with Google Cloud Platform (GCP) services, including Cloud Storage, Dataflow, Pub/Sub, BigQuery, Cloud Spanner and Cloud Functions
- Develop and manage data APIs and interfaces (REST and GraphQL) to enable high-performance data access across microservices.
- Implement CI/CD automation for data pipelines using GitHub Actions, Argo CD, or equivalent tools.
- Collaborate with Data Scientists and MLOps teams to integrate ML/NLP models into data pipelines and production workflows.
- Build and operationalize NLP data pipelines for structured and unstructured data sources (e.g., Rx claims, clinical documents).
- Enable continuous learning and model‑retraining workflows using Vertex AI, Kubeflow, or similar GCP‑native tooling.
- Implement frameworks for observability and data quality, ensuring ML predictions, confidence scores, and fallback events are logged into data lakes or monitoring systems.
- Support distributed data systems and ensure reliability, performance, and scalability of data infrastructure.
Required Qualifications
- 5+ years of experience building data pipelines or backend data workflows using Python, Java, or similar languages.
- 2+ years of experience designing REST/GraphQL data services or integrating data APIs.
- Hands‑on experience working with ML/AI model integration in production (e.g., Vertex AI Endpoints, TensorFlow Serving, ML REST APIs).
- Experience handling structured and unstructured datasets, including healthcare data (Rx claims, clinical documents, NLP text).
- Familiarity with the end-to-end ML lifecycle: data ingestion, feature engineering, training, deployment, and real‑time inference.
- 2+ years of experience with cloud platforms (GCP preferred; AWS or Azure acceptable).
- 2+ years working with streaming platforms like Kafka or equivalent.
- 2+ years of experience with databases (Postgres or similar relational systems).
- 2+ years of experience with CI/CD tools (GitHub Actions, Jenkins, Argo CD, etc.).
Preferred Qualifications
- Direct, hands-on experience with Google Cloud Platform, especially BigQuery, Dataflow, GKE, Composer and Vertex AI.
- Knowledge of Kubernetes concepts and experience running data services or pipelines on GKE.
- Strong understanding of distributed systems, microservice patterns, and data‑centric system design.
- Experience using Vertex AI, Kubeflow, or other ML orchestration platforms for model training and serving.
- Knowledge of GenAI pipelines, LLM prompt workflows, and agent orchestration frameworks (e.g., LangChain, transformers).
- Experience deploying Python-based ML/NLP services into microservice ecosystems using REST, gRPC, or sidecar architectures.
- Domain experience in healthcare, claim adjudication, or Rx data processing.
Education
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or equivalent experience
(High School Diploma + 4 years of relevant experience acceptable).
Compensation, Benefits and Duration
Minimum Compensation: USD 42,000
Maximum Compensation: USD 147,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full-time employees.
This position is available for independent contractors
No applications will be considered if received more than 120 days after the date of this post
Data Engineering work from LinkedIn, X, Reddit and Upwork, in one list.
Each morning you get everything that fits, strongest first.
Data Engineering$40k+
Find me workBy the team behind dataroles.