Omni's team is passionate about Commerce and Digital Transformation. We've been successfully delivering Commerce solutions for clients across North America, Europe, Asia, and Australia. The team has experience executing and delivering projects in B2B and B2C solutions. Job Description This is a remote position. We are seeking a Senior Data Engineer to architect and build robust, scalable, and efficient data systems that power AI and Analytics solutions. You will design end-to-end data pipelines, optimize data storage, and ensure seamless data availability for machine learning and business analytics use cases. This role demands deep engineering excellence balancing performance, reliability, security, and cost to support real-world AI applications. Key Responsibilities Architect, design, and implement high-throughput ETL/ELT pipelines for batch and real-time data processing. Build cloud-native data platforms : data lakes, data warehouses, feature stores. Work with structured, semi-structured, and unstructured data at petabyte scale. Optimize data pipelines for latency, throughput, cost-efficiency, and fault tolerance. Implement data governance, lineage, quality checks, and metadata management. Collaborate closely with Data Scientists and ML Engineers to prepare data pipelines for model training and inference. Implement streaming data architectures using Kafka, Spark Streaming, or AWS Kinesis. Automate infrastructure deployment using Terraform, CloudFormation, or Kubernetes operators. Requirements 7+ years in Data Engineering, Big Data, or Cloud Data Platform roles. Strong proficiency in Python and SQL. Deep expertise in distributed data systems (Spark, Hive, Presto, Dask). Cloud-native engineering experience (AWS, GCP, Azure) : BigQuery, Redshift, EMR, Databricks, etc. Experience designing event-driven architectures and streaming systems (Kafka, Pub/Sub, Flink). Strong background in data modeling (star schema, OLAP cubes, graph databases). Proven experience with data security, encryption, compliance standards (e.g., GDPR, HIPAA). Preferred Skills Experience in MLOps enablement : creating feature stores, versioned datasets. Familiarity with real-time analytics platforms (Clickhouse, Apache Pinot). Exposure to data observability tools like Monte Carlo, Databand, or similar. Passionate about building high-scale, resilient, and secure data systems. Excited to support AI/ML innovation with state-of-the-art data infrastructure. Obsessed with automation, scalability, and best engineering practices. (ref:hirist.tech) Show more Show less