Data engineer

2 hours ago

Agra, India Digitalzone Full time

About the Role As a Data Engineer, you will design, build, and optimize data pipelines and real-time systems that power AI-driven decisioning and analytics. Responsibilities Develop and maintain scalable ETL/ELT pipelines using Python, Airflow, and dbt Build and optimize real-time streaming pipelines using Kafka, Rabbit MQ, Spark and event-driven microservices Leverage AI coding agents (Claude Code, Cursor AI, Git Hub Copilot) to accelerate data pipeline development, data transformation, and optimization Design and manage data storage solutions with Postgre SQL, Click House and Elasticsearch Define and implement data modeling best practices for efficient and scalable data storage. Ensure high availability, performance, and security of data infrastructure on AWS cloud Design and implement BI-ready datasets in Click House optimized for OLAP workloads and reporting use cases. Develop and manage denormalized schemas and data models to support analytics and am Charts-based reporting. Optimize query performance, partitioning, and indexing strategies in Click House to handle large-scale analytical workloads. Implement best practices for data quality, validation, and observability Work on data ingestion using Airbyte or other connector frameworks Implement data transformation pipelines supporting analytical and ML workloads Collaborate with cloud and Dev Ops teams for containerized deployments (Docker, Kubernetes, AWS) Support integration of ML workflows and model APIs into production pipelines Qualifications 6+ years of hands-on experience in data engineering and distributed systems. Required Skills Experience with AI-assisted coding tools (Claude Code, Git Hub Copilot, Cursor AI) Strong expertise in Click House for analytics, including data modeling, partitioning, indexing, and query optimization. Proven ability to design and manage denormalized OLAP datasets to support BI and reporting workloads. Solid understanding of ETL/ELT pipelines and experience with tools such as Airbyte, DBT, Airflow or Python-based transformations. Proficiency in SQL (advanced queries, performance tuning, window functions, aggregations). Hands-on experience working with reporting/visualization frameworks (e.g., am Charts or similar libraries). Strong foundation in data quality, governance, and validation to ensure reliable analytics. Strong knowledge of real-time data streaming (Kafka, Spark) Experience with relational and analytical databases (Postgres, Click House, Elasticsearch) Exposure to ML model integration, MLOps is a strong plus Strong problem-solving, debugging, and cross-team collaboration skills Preferred Skills Experience with relational and analytical databases (Postgres, Click House, Elasticsearch) Exposure to ML model integration, MLOps is a strong plus

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