Data Engineer-ETL (S03)
Systango Technologies LimitedMatch Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessPopular mid-level Data Engineer role with broad cloud/ETL stack attracts many candidates.
Core data engineering skills (Spark, SQL, Python, cloud) are easily transferable across industries.
Explicit 5–7 years and extensive mandatory cloud, ETL, and orchestration skills make screening strict.
Job Description
Structured overview of role & requirementsAbout 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.