Data Engineer

Data Engineer

Role Summary

We are seeking a Data Engineer to design, build, and maintain scalable data platforms that

power analytics, reporting, and operational decision-making. The role is responsible for

data pipelines, warehouse architecture, Change Data Capture (CDC), and real-time data

integration, ensuring data is reliable, accessible, and optimized for business use.

Key Responsibilities

● Design and maintain end-to-end data architecture from source systems to analytics

platforms.

● Build and manage ETL/ELT pipelines using Apache Airflow.

● Develop and optimize data warehouses in Google BigQuery and ClickHouse.

● Implement and support CDC solutions using Debezium, Kafka, and Altinity Sink

Connector.

● Manage and monitor Kafka infrastructure through Strimzi Operator on

Kubernetes/EKS.

● Design and maintain scalable data models, fact tables, and dimension tables.

● Optimize ClickHouse performance through proper partitioning, indexing, storage

design, and query tuning.

● Implement data quality, monitoring, alerting, and observability frameworks.

● Ensure platform reliability, scalability, and cost efficiency.

● Collaborate with Analytics, Engineering, Product, and Business teams to deliver

trusted data solutions.

Required Qualifications

● Data Engineering experience.

● Advanced SQL and data modeling skills.

● Hands-on experience with:

○ Google BigQuery

○ ClickHouse

○ Apache Airflow

○ Apache Kafka

○ Debezium CDC

● Experience building large-scale ETL/ELT pipelines.

● Understanding of distributed systems, CDC, and event-driven architectures.

● Experience with cloud platforms and containerized environments.

● Strong troubleshooting and performance optimization skills.

Preferred Qualifications

● Experience with Strimzi Operator.

● Experience with Altinity Sink Connector.

● Python, dbt, Terraform, and GCP experience.

● Experience supporting Metabase, or other BI platforms.

Technology Stack

ClickHouse • BigQuery • Airflow • Kafka • Debezium • Altinity Sink Connector • Strimzi •

Kubernetes (EKS) • SQL • Python • AWS

Success Measures

● Reliable and scalable data pipelines.

● High-performance ClickHouse and BigQuery environments.

● Low-latency CDC data ingestion.

● High data quality and platform uptime.

● Optimized warehouse cost and performance.

● Timely delivery of data solutions for analytics and reporting.

Key Positioning

The Data Engineer owns the organization's data platform, including BigQuery, ClickHouse,

Airflow, Kafka, and CDC architecture, providing the foundation for analytics, reporting, and

data-driven decision-making.