Data Engineer (SQL+Python+AWS)

Fidelity International; Bengaluru; On-site; INR 1,500,000 - 3,500,000; Full time

Job summary

Fidelity International in Bengaluru seeks a data engineering professional to design and maintain scalable data pipelines and data models. You will leverage SQL, Python, and cloud services to transform and integrate data across platforms while ensuring data quality and governance.

The role emphasizes building robust batch/streaming pipelines with modern orchestration tools and contributing to lakehouse architectures supporting analytics and BI initiatives.

Qualifications

• Proficient in SQL and data modelling for warehousing/lakehouse environments.
• Hands-on Python for scalable ETL/ELT pipelines and automation.
• Experience with major cloud platforms and managed data services.
• Strong knowledge of data pipelines, orchestration, and architecture.
• Familiarity with Airflow, dbt, Spark, or Kafka for batch/streaming data.
• Understanding of data lakes, warehouses, lakehouses and medallion layers.

Skills

Strong SQL and Data Modelling

Python for ETL/ELT

Cloud ecosystems (AWS, Azure, GCP)

Modern data pipeline/orchestration

Airflow / dbt / Spark / Kafka

Data warehouse / lakehouse

Modern data architecture concepts

Tools

Airflow

dbt

Spark

Kafka

Terraform

Docker

Kubernetes

Job description

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.
• Proficiency in Python for building scalable ETL/ELT pipelines, data processing workflows, automation, and integration across data platforms.
• 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.
• 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.