Data Engineer (SQL+Python+AWS)
Fidelity International · Bangalore Bazaar, India ·
- Category
- Data engineering
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Data Engineer at Fidelity International in Bangalore building and maintaining scalable data pipelines and warehouse/lakehouse layers. Day to day involves complex SQL, Python ETL/ELT development, and cloud data platforms (AWS/Azure/GCP) using orchestration tools like Airflow, dbt, Spark, and Kafka.
We are looking for someone with the following skills :
1. Strong SQL and Data Modelling skills
Hands-on experience writing complex SQL for data transformation, analytics, and performance optimization, with solid understanding of data modelling principles for warehouse/lake-house environments.
2. Python / modern programming for data engineering
Proficiency in Python for building scalable ETL/ELT pipelines, data processing workflows, automation, and integration across data platforms.
3. Cloud data platform experience
Practical experience with modern cloud ecosystems such as AWS, Azure, or GCP, including managed data services, storage, compute, and orchestration patterns.
4. Modern data pipeline and orchestration tools
Experience designing and maintaining reliable batch and/or streaming pipelines using tools such as Airflow, dbt, Spark, Kafka, or equivalent modern data stack technologies.
5. Data warehouse / lakehouse architecture knowledge
Strong understanding of modern data architecture concepts including data lakes, data warehouses, lakehouses, medallion-style layering, data quality, and scalable ingestion/serving patterns.
Good to Have Skills
1. Experience with big data / distributed processing frameworks
Exposure to Spark, Databricks, Flink, or similar technologies for large-scale data transformation and processing.
2. Data governance and data quality practices
Familiarity with lineage, cataloging, schema management, observability, data testing, and governance controls.
3. CI/CD and infrastructure-as-code for data platforms
Experience with Git-based workflows, Terraform, Docker, Kubernetes, and deployment automation for data engineering solutions.
4. Streaming / real-time data experience
Knowledge of event-driven architectures and real-time ingestion/processing using Kafka, Kinesis, Pub/Sub, or similar tools.
5. Business intelligence and analytics enablement
Ability to work closely with analytics, product, and business teams to deliver curated datasets that support reporting, dashboards, and self-service analytics.