Data Engineering (Level - SDE 3)

Cogoport Story  

Cogoport is on a mission to bridge the $3.4 trillion Trade Knowledge and Execution Gap, empowering businesses worldwide by simplifying global trade. As a leading Global Trade Platform, we help companies connect with trade partners, optimize logistics, and improve cash flow. Recognized as an Asia-Pacific High-Growth Company (Financial Times) and an Indian Growth Champion (Economic Times), we are expanding across India, China, Vietnam, Singapore, Thailand, and Indonesia.   

Why Join Us?   

At Cogoport, you'll work with some of the brightest minds in the industry, driving digital transformation in logistics. We foster an entrepreneurial culture, where innovation, impact, and career growth go hand in hand.  
 

• Impact: Work on real-world logistics challenges that directly optimize global trade and supply chain efficiencies. 

• Innovation: Develop AI-driven logistics solutions that improve freight booking, shipment tracking, and cost optimization. 

• Collaboration: Work with cross-functional teams across data science, engineering, and product to deliver impactful solutions.   

• Growth: Be part of a fast-growing company and scale next-gen logistics platforms using advanced data engineering and AI.  
  
  

Join us in revolutionizing digital freight and logistics!  

About the role?   

We are looking for a Senior Data Engineer (SDE 3) to build scalable, high-performance data solutions that drive actionable insights for logistics, freight pricing, shipment tracking, and operational efficiencies.  

Key Responsibilities:   

1. Scalable Data Pipeline Development   

• Design and develop real-time and batch ETL/ELT pipelines for structured and unstructured logistics data (freight rates, shipping schedules, tracking events, etc.).   

• Optimize data ingestion, transformation, and storage for high availability and cost efficiency   

• Ensure seamless integration of data from global trade platforms, carrier APIs, and operational databases.  
  
  

2. Data Architecture & Infrastructure

• Architect scalable, cloud-native data platforms using AWS (S3, Glue, EMR, Redshift), GCP (BigQuery, Dataflow), or Azure.   

• Build and manage data lakes, warehouses, and real-time processing frameworks to support analytics, machine learning, and reporting needs.  
• Optimize distributed databases (Snowflake, Redshift, BigQuery, Apache Hive) for logistics analytics  

3. Real-time & Predictive Analytics   

• Develop streaming data solutions using Apache Kafka, Pulsar, or Kinesis to power real-time shipment tracking, anomaly detection, and dynamic pricing.   

• Enable AI-driven freight rate predictions, demand forecasting, and shipment delay analytics.   

• Improve customer experience by providing real-time visibility into supply chain disruptions and delivery timeline  

4. Performance Optimization & Data Governance   

• Ensure high availability, fault tolerance, and data security compliance (GDPR, CCPA) across the platform.   

• Improve query performance for large-scale logistics datasets through indexing, partitioning, and caching techniques.   

• Implement data quality checks, lineage tracking, and metadata management for transparency and governance.   

5. Collaboration & Leadership   

• Work closely with product managers, data scientists, and logistics experts to translate business needs into scalable data solutions.   

• Partner with business teams to deliver revenue-impacting insights on freight, shipments, and trade patterns
    

Required Qualifications:

• 6+ years of experience in data engineering, working with large-scale distributed systems.   

• Strong proficiency in Python, Java, or Scala for data processing   

• Expertise in SQL and NoSQL databases (PostgreSQL, Cassandra, Snowflake, Apache Hive, Redshift).  

• Experience with big data processing frameworks (Apache Spark, Flink, Hadoop).  

• Hands-on experience with real-time data streaming (Kafka, Kinesis, Pulsar) for logistics use cases.   

• Deep knowledge of AWS/GCP/Azure cloud data services like S3, Glue, EMR, Databricks, or equivalent.   

• Familiarity with Airflow, Prefect, or Dagster for workflow orchestration.   

• Strong understanding of logistics and supply chain data structures, including freight pricing models, carrier APIs, and shipment tracking systems.  

Preferred Qualifications   

• Experience in building predictive analytics solutions for freight rates, demand forecasting, and risk assessment.   

• Experience in machine learning model deployment and MLOps practices.   

• Exposure to real-time analytics and OLAP systems.   

• Certifications in AWS/GCP Data Engineering.