Data Engineer-ETL (S03)

About US

Systango Technologies Limited (NSE: SYSTANGO) is a digital engineering company that offers enterprise-class IT and product engineering services to different size organizations. At Systango, we have a culture of efficiency - we use the best-in-breed technologies to commit quality at speed and world-class support to address critical business challenges. We leverage Gen AI, AI/Machine Learning and Blockchain to unlock the next stage of digitalization for traditional businesses. Our handpicked team is adept at web & enterprise development, mobile apps, QA and DevOps. Sila, Cuentas, Youtility, Porsche, MGM Grand, Deloitte, Grindr, and Tawk.to are some of the top clients that have entrusted us to enhance their digital capabilities and build disruptive innovations. We believe in making the impossible, Possible and we do it literally.

About The Role

We are looking for a Data Engineer with 5–7 years of experience to build and scale our modern data platform. You will design reliable, scalable, and secure data pipelines that power analytics, reporting, machine learning, and business applications. You will work closely with software engineers, product teams, and data scientists to deliver high-quality data solutions.

Key Responsibilities

• Design, build, and maintain scalable batch and real-time data pipelines.
• Develop ETL/ELT workflows to ingest, transform, and integrate data from multiple internal and external sources.
• Build and maintain data warehouse/lakehouse solutions to support analytics and AI/ML workloads.
• Develop robust data models and transformation pipelines using SQL and Python.
• Optimize data storage, query performance, and processing efficiency across cloud platforms.
• Collaborate with Data Science and ML teams to build production-ready datasets and feature pipelines.
• Implement monitoring, testing, and automation to ensure reliability and scalability of data pipelines.
• Contribute to data architecture, engineering standards, and best practices while ensuring secure and compliant data handling.
  
  

Required Skills

Data Engineering

• Strong experience designing and building production-grade ETL/ELT pipelines.
• Experience with batch and streaming data processing.
• Good understanding of data modeling, warehouse/lakehouse architecture, and performance optimization.
  
  

Programming

• Strong Python programming skills.
• Advanced SQL.
• Hands-on experience with Apache Spark (PySpark preferred).
• Scala is a plus.
  
  

Data Platforms & Warehouses

• Experience with one or more cloud ecosystems and modern analytical platforms:
• AWS, Azure, or GCP
• Snowflake, Databricks, Amazon Redshift, Google BigQuery, or Azure Synapse
• Lakehouse technologies such as Delta Lake, Apache Iceberg, or Apache Hudi
  
  

Data Processing & Orchestration

• Apache Spark
• Apache Kafka or AWS Kinesis
• Airflow, Dagster, or Prefect
• dbt
  
  

Databases

• PostgreSQL, MySQL, or other relational databases
• Experience with NoSQL databases such as MongoDB is a plus.
  
  

DevOps

• Docker and Kubernetes
• Infrastructure as Code (Terraform preferred)
• CI/CD for data engineering workflows
  
  

Preferred Qualifications

• 5–7 years of hands-on experience in Data Engineering.
• Experience building cloud-native data platforms at scale.
• Exposure to AI/ML data pipelines is preferred.
• Experience in fintech, banking, SaaS, or other data-intensive products is a plus.
• Strong problem-solving skills and the ability to work independently in a fast-paced environment.