Data Engineer

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, RabbitMQ, Spark and event-driven microservices
• Leverage AI coding agents (Claude Code, Cursor AI, GitHub Copilot) to accelerate data pipeline development, data transformation, and optimization
• Design and manage data storage solutions with PostgreSQL, ClickHouse 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 ClickHouse optimized for OLAP workloads and reporting use cases.
• Develop and manage denormalized schemas and data models to support analytics and amCharts-based reporting.
• Optimize query performance, partitioning, and indexing strategies in ClickHouse 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 DevOps 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, GitHub Copilot, Cursor AI)
• Strong expertise in ClickHouse for analytics, including data modeling, partitioning, indexing, and query optimization.

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  Get Hired
• 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., amCharts 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, ClickHouse, 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, ClickHouse, Elasticsearch)
• Exposure to ML model integration, MLOps is a strong plus