Data Platform Engineer

SRS Consulting Inc
Sunnyvale, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Sunnyvale, United States of America

Tech stack

Java
Amazon Web Services (AWS)
Business Analytics Applications
Data analysis
Azure
Google BigQuery
Continuous Integration
Data Governance
Data Infrastructure
DevOps
Hadoop
Monitoring of Systems
Machine Learning
NoSQL
Cloud Services
TensorFlow
Standard Sql
Azure
Google Cloud Platform
Enterprise Software Applications
Data Ingestion
Snowflake
Spark
Spring-boot
Backend
Containerization
Kubernetes
Apache Flink
Data Analytics
Real Time Data
Kafka
Data Management
Machine Learning Operations
REST
Docker
Redshift
Microservices

Job description

  • Data Platform Development: Design, build, and maintain scalable data platforms to support analytics and ML workloads.
  • Machine Learning Infrastructure: Develop and optimize ML pipelines, model deployment frameworks, and monitoring systems.
  • Data Analytics Solutions: Implement data ingestion, transformation, and visualization pipelines to enable business insights.
  • Backend Engineering: Build robust APIs and services in Java to integrate data platforms with enterprise applications.
  • Cloud & DevOps: Deploy and manage solutions on cloud platforms (AWS, Azure, Google Cloud Platform) with CI/CD automation.
  • Collaboration: Work closely with data scientists, analysts, and product teams to deliver end-to-end solutions.

Requirements

We are seeking a highly skilled Data Platform Engineer with strong expertise in building and scaling Machine Learning (ML) and Data Analytics platforms. The ideal candidate will also bring solid experience in Java backend engineering, enabling seamless integration of data systems with enterprise applications., * Strong expertise in Java backend development(Spring Boot, REST APIs, microservices).

  • Hands-on experience with data platforms(Spark, Hadoop, Kafka, Flink, or similar).
  • Proficiency in ML platforms(TensorFlow Serving, MLflow, Kubeflow, or similar).
  • Solid understanding of data analytics tools(Snowflake, BigQuery, Redshift, or equivalent).
  • Experience with cloud services(AWS/Google Cloud Platform/Azure) and containerization (Docker, Kubernetes).
  • Knowledge of SQL/NoSQL databasesand data modeling.
  • Strong problem-solving and communication skills.

Preferred Qualifications

  • Experience in real-time data processingand streaming architectures.
  • Familiarity with data governance, security, and compliance frameworks.
  • Exposure to MLOps practicesand automated model lifecycle management.

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