Senior Data Engineer

.Kreate

Senior Data Engineer at Kreate transforming data into scalable, reliable platforms for analytics and machine learning. Bridging ML experimentation and production deployment to empower decision-making.

Posted 6/3/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $150,000 - $170,000 per yearWebsite

Tech Stack

Tools & technologies

AzureCloudKafkaPythonSparkSQL

About the role

Key responsibilities & impact

Deliver production-ready datasets and pipelines to support data science and analytics.
Bridge the gap between ML experimentation and deployment with MLOps best practices.
Build and deploy data-driven applications using Azure services.
Maximize ROI on Azure investment through cloud-native architecture.
Standardize development workflows using GitHub (version control, pull requests, CI/CD).
Automate deployments and accelerate engineering velocity with GitHub Actions.
Reduce bottlenecks for analysts, engineers, and data scientists by improving data accessibility and workflow efficiency.
Enhance data quality, governance, and observability.
Enable future capabilities such as AI, automation, and personalization.

Requirements

What you’ll need

5+ years of experience in data engineering or related roles.
Proven experience in data engineering, cloud architecture, and MLOps.
Strong proficiency in Python and SQL for data pipelines, automation, and data framework (Spark, Panda).
Familiarity with REST APIs and application development concepts.
Strong knowledge of Azure services (Data Factory, Synapse, Databricks, etc.) and cloud-native solutions.
Proficiency with GitHub, including version control, CI/CD, and automation using GitHub Actions, workflows, and PR reviews.
Experience delivering production-ready datasets and pipelines for analytics and ML.
Ability to collaborate effectively with data scientists, analysts, and engineers to prepare and deliver clean, feature-ready datasets.
Knowledge with data governance, observability, and quality best practices is preferred.
Experience with real-time streaming tools (Kafka, Event Hub) also preferred.
Strong problem-solving skills and a passion for building scalable, maintainable, and automated data platforms.
Experience with Microsoft Fabric of Lakehouse architectures is a nice-to-have.

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account .Kreate Website LinkedIn All Job Openings 501 - 1000 employees Founded 1997 ⚡ Energy 🤖 Artificial Intelligence ☁️ SaaS Energy
Artificial Intelligence
SaaS Kreate is an India-based energy technology and trading company that combines power trading, renewable energy services, and software products. It operates as a registered trading and clearing member of Indian Energy Exchange (IEX) and Power Exchange India Limited (PXIL), facilitating short- and long-term power sale/purchase, open access transactions, and Renewable Energy Certificate (REC) trading. The company develops AI/ML and statistics-driven forecasting and analytics platforms (e. g. , RED Fx) for accurate renewable generation and demand forecasts, and offers a suite of energy-focused software products including trading, scheduling, energy accounting, real-time monitoring (RealGen), and EMS solutions. . Kreate serves utilities, generators, industrial and commercial clients and is organized into divisions such as Kreate Technologies, Kreate Energy, Kreate Lights, and Kreate Commodity. Senior Data Engineer Job not on LinkedIn 🔥 0 minutes ago 🇺🇸 United States – Remote 💵 $150k - $170k / year ⏰ Full Time 🟠 Senior 🚰 Data Engineer Azure Cloud Kafka Python Spark SQL Apply Now Find Hiring Managers Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Deliver production-ready datasets and pipelines to support data science and analytics.
Bridge the gap between ML experimentation and deployment with MLOps best practices.
Build and deploy data-driven applications using Azure services.
Maximize ROI on Azure investment through cloud-native architecture.
Standardize development workflows using GitHub (version control, pull requests, CI/CD).
Automate deployments and accelerate engineering velocity with GitHub Actions.
Reduce bottlenecks for analysts, engineers, and data scientists by improving data accessibility and workflow efficiency.
Enhance data quality, governance, and observability.
Enable future capabilities such as AI, automation, and personalization. 🎯 Requirements
5+ years of experience in data engineering or related roles.
Proven experience in data engineering, cloud architecture, and MLOps.
Strong proficiency in Python and SQL for data pipelines, automation, and data framework (Spark, Panda).
Familiarity with REST APIs and application development concepts.
Strong knowledge of Azure services (Data Factory, Synapse, Databricks, etc.) and cloud-native solutions.
Proficiency with GitHub, including version control, CI/CD, and automation using GitHub Actions, workflows, and PR reviews.
Experience delivering production-ready datasets and pipelines for analytics and ML.
Ability to collaborate effectively with data scientists, analysts, and engineers to prepare and deliver clean, feature-ready datasets.
Knowledge with data governance, observability, and quality best practices is preferred.
Experience with real-time streaming tools (Kafka, Event Hub) also preferred.
Strong problem-solving skills and a passion for building scalable, maintainable, and automated data platforms.
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Hard Skills & Tools

PythonSQLMLOpsData EngineeringCloud ArchitectureSparkPandasREST APIsGitHubReal-time Streaming

Soft Skills

Problem-solvingCollaborationWorkflow EfficiencyData AccessibilityData QualityGovernanceObservability