ETL Data Engineer | Onsite

Photon•United States,

4d•$42,000 - $147,000•Onsite

About The Position

We are seeking an experienced ETL Data Engineer to join our team. This role involves designing, building, and maintaining scalable data pipelines to support analytics, ML, and operational reporting. You will develop robust data ingestion, transformation, and integration workflows using Python, SQL, and modern data engineering frameworks. The position requires building and maintaining batch and streaming data pipelines, working with Google Cloud Platform (GCP) services, and developing data APIs. You will also implement CI/CD automation, collaborate with Data Scientists and MLOps teams, and build NLP data pipelines. This role is crucial for enabling continuous learning and model-retraining workflows, implementing frameworks for observability and data quality, and supporting distributed data systems to ensure reliability, performance, and scalability.

Requirements

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.).

Nice To Haves

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.

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.