Data Engineer-ETL (S03)
Systango Technologies Limited (company site, opens in a new tab)5–7 yrsIndoreOn-siteDirect career pageEst. 12–22 LPA
Posted on 21 Sept
Reach people at Systango Technologies Limited
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Systango Technologies Limited you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Build and scale reliable, scalable, and secure batch and real-time data pipelines supporting analytics, reporting, and AI/ML applications.
- Design, develop and optimize ETL/ELT workflows and data warehouse/lakehouse solutions using SQL, Python, and Spark.
- Collaborate with data scientists and engineering teams to deliver high-quality production-ready datasets and maintain data platform standards and security.
Minimum Requirements
- 5–7 years of hands-on experience in Data Engineering.
- Strong skills in Python programming, advanced SQL, and Apache Spark (PySpark preferred).
- Experience with cloud platforms (AWS, Azure, or GCP) and modern data warehouses/lakehouse technologies (Snowflake, Databricks, Redshift, BigQuery, Delta Lake, etc.).
- Experience building scalable ETL/ELT pipelines, batch and streaming data processing, and working with orchestration tools (Airflow, Dagster, or Prefect).
Ideal Candidate Profile
- Experienced in developing and optimizing cloud-native data platforms at scale with focus on reliability and performance.
- Able to independently design and implement end-to-end data engineering solutions that serve analytics and AI/ML teams.
- Preferable background in fintech, banking, SaaS, or other data-intensive product environments with exposure to AI/ML data pipelines.