ETL Data Engineer | Onsite

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