On the Job
- Build & Optimize Data Pipelines: Design, develop, and maintain scalable ETL workflows for processing structured and unstructured data.
- ClickHouse Expertise: Implement, optimize, and manage ClickHouse for efficient real-time analytics and reporting.
- Big Data Processing: Experience working with large-scale data architectures using Spark, Kafka, Hadoop, Flink or similar tools.
- Data Modeling & Warehousing: Design efficient data schemas, partitioning, and indexing strategies for optimal query performance.
- Real-Time Data Streaming: Implement streaming solutions using Kafka, Spark Streaming, or Flink to enable low-latency data processing.
- Cloud Data Infrastructure: Deploy and manage data solutions on AWS, GCP, or Azure with tools like S3, Redshift, BigQuery, or Snowflake.
- Automation & Monitoring: Develop robust monitoring systems and automate workflows using Airflow, Prefect, or Kubernetes.
- Data Security & Governance: Ensure compliance with GDPR, CCPA, and best practices for data integrity and security.
- Collaboration: Work closely with Data Scientists, Analysts, and Software Engineers to integrate data solutions into applications.
Must-haves (Min. qualifications)
- 3-5 years of experience in data engineering or related roles.
- Strong hands-on experience with ClickHouse for real-time data processing and analytics.
- Proficiency in Python, SQL, and familiarity with Scala or Java.
- Experience with Kafka, Spark, Hadoop, or Flink for big data processing.
- Expertise in ETL/ELT pipeline development and optimization.
- Hands-on experience with cloud-based data architectures (primarily AWS).
- Strong understanding of database performance tuning, indexing, and partitioning.
- Experience with workflow orchestration tools like Airflow etc.
- Solid understanding of SQL databases like Postgres and proficiency in writing SQL queries.
Good to have
- Experience working in the fintech domain or handling financial data.
- Knowledge of real-time data warehousing solutions.
- Hands-on experience with Kubernetes, Terraform, or Docker for data infrastructure.
- Familiarity with data observability tools for monitoring and debugging pipelines.
- Exposure to machine learning model deployment pipelines.
Why join us?
- Work on cutting-edge big data and real-time analytics in the fintech space.
- Opportunity to lead and scale high-impact data solutions.
- Collaborative, fast-paced, and growth-driven work environment.
- Competitive salary, benefits, and career growth opportunities.
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