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Omni Reach - Senior Data Engineer - Python/ETL Job Opening In India, India – Now Hiring Omni Reach


Job description

<p><p>We are seeking a Senior Data Engineer to build scalable, cloud-native data platforms and enable end-to-end MLOps workflows.

You will design ETL/ELT pipelines, manage data lakes/warehouses/feature stores, and ensure high-performance, secure, and cost-efficient pipelines for AI/ML and analytics.

This role blends Data Engineering + MLOps to deliver production-ready, automated, and reliable ML workflows.<br/><br/><b>Responsibilities :</b><br/><br/>- Data Pipelines : Design & optimize batch/streaming ETL/ELT pipelines at scale.<br/><br/>- Platforms : Build/manage data lakes, warehouses, feature stores for ML/BI workloads.<br/><br/>- MLOps : Enable model training, deployment, CI/CD, monitoring, retraining, versioning using SageMaker (AWS), Vertex AI (GCP), Azure ML.<br/><br/>- Streaming : Implement real-time pipelines with Kafka, Spark Streaming, AWS Kinesis, GCP Pub/Sub, Azure Event Hubs.<br/><br/>- Automation : Leverage Terraform, CloudFormation, ARM, Kubernetes for infra-as-code & scaling.<br/><br/>- Quality & Governance : Ensure data lineage, metadata, observability, security, compliance, cost efficiency.<br/><br/>- Collaboration : Work with Data Scientists & ML Engineers to productionize ML models across cloud environments.<br/><br/><b>Required Skills :</b><br/><br/>- 5+ years of handson experience in Data Engineering, Big Data, or Cloud Data Platform roles, working on large scale production systems.<br/><br/></p><p>- Strong command of Python and SQL, using them to build and optimize ETL/ELT pipelines.<br/><br/></p><p>- Deep working knowledge of distributed data systems (e.g., Spark, Hive, Presto, Dask) for batch and real-time processing.<br/><br/></p><p>- Proven track record with cloud-native platforms across AWS, GCP, or Azure e.g., BigQuery, Redshift, EMR, Databricks for data storage and analytics.<br/><br/></p><p>- Experience designing and maintaining event driven and streaming architectures (Kafka, Pub/Sub, Flink).<br/><br/></p><p>- Solid background in data modeling (star schema, OLAP cubes, graph databases) to support BI and analytics.<br/><br/></p><p>- Practical exposure to data security, encryption, and compliance frameworks (e.g., GDPR, HIPAA).<br/><br/><b>Preferred Skills :</b><br/><br/>- Direct experience enabling MLOps workflows building feature stores, managing versioned datasets, or integrating pipelines with ML platforms (SageMaker, Vertex AI, Azure ML).<br/><br/></p><p>- Familiarity with real-time analytics systems such as Clickhouse or Apache Pinot.</p><p><br/></p><p>- Exposure to data observability tools (e.g., Monte Carlo, Databand) to monitor quality, lineage, and reliability.<br/><br/></p><p>- Demonstrated ability to build scalable, resilient, and secure data systems that support mission critical applications.<br/><br/></p><p>- Interest and experience in supporting AI/ML innovation with robust data infrastructure.<br/><br/></p><p>- Strong mindset for automation, scalability, DevOps/MLOps practices, and engineering excellence.</p><br/></p> (ref:hirist.tech)

Required Skill Profession

Computer Occupations


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