Senior Data Engineer

This role is for one of the Weekday's clients

Min Experience: 7 years

JobType: full-time

We are looking for a highly skilled Senior Data Engineer to architect and develop robust, scalable data infrastructure that underpins cutting-edge AI and analytics solutions. In this role, you will design and build high-performance data pipelines, optimize data storage, and ensure the seamless availability of data for machine learning and business intelligence applications. The ideal candidate combines strong engineering fundamentals with deep expertise in cloud data platforms, real-time processing, and data governance.

Requirements

• Architect and implement scalable ETL/ELT pipelines for both batch and streaming data.
• Design and build cloud-native data platforms, including data lakes, data warehouses, and feature stores.
• Work with diverse data typesstructured, semi-structured, and unstructuredat petabyte scale.
• Optimize data pipelines for high throughput, low latency, cost-efficiency, and fault tolerance.
• Ensure strong data governance through lineage tracking, quality validation, and metadata management.
• Collaborate with Data Scientists and ML Engineers to prepare datasets for training, inference, and production use.
• Build and maintain streaming data architectures using technologies like Kafka, Spark Streaming, or AWS Kinesis.
• Automate infrastructure provisioning and deployment using tools like Terraform, CloudFormation, or Kubernetes operators.

• 7+ years of experience in Data Engineering, Big Data, or cloud-based data platforms.
• Strong coding skills in Python and SQL.
• Deep understanding of distributed data processing systems (e.g., Spark, Hive, Presto, Dask).
• Hands-on experience with cloud services (AWS, GCP, Azure) and tools such as BigQuery, Redshift, EMR, or Databricks.
• Experience building event-driven and real-time data systems (e.g., Kafka, Pub/Sub, Flink).
• Proficiency in data modeling (e.g., star schema, OLAP cubes, graph databases).
• Knowledge of data security, encryption, and regulatory compliance (e.g., GDPR, HIPAA).

• Experience enabling MLOps workflows through the development of feature stores and versioned datasets.
• Familiarity with real-time analytics tools such as ClickHouse or Apache Pinot.
• Exposure to data observability tools like Monte Carlo, Databand, or similar platforms.
• Strong passion for building resilient, secure, and scalable data systems.
• Keen interest in enabling AI/ML innovation through robust infrastructure.
• Committed to automation, performance optimization, and engineering excellence.