Omni Reach - Senior Data Engineer - Python/ETL

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.
  

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.
  

(ref:hirist.tech)