OmniReach

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