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Data Engineer (Mid) - 4777

IT · Development Team

Overview

Location: onsite · Hyderabad

Employment Type: full time

Experience: 6+ years

Compensation: INR 1,025,280 - 1,281,600

Interview Process:

L1: Technical interview with Engineer (Virtual)

L2: SME interview (Virtual)

L3: Final round with Manager (Onsite)

Job Title

Data Engineer (Mid)

Experience Required

Extracted: 6+ years of hands-on experience building enterprise-scale applications, data platforms, or

distributed systems.

Solid experience in Python (5+ years)

Big Data (6+ years)

Hands-on expertise with AWS services, particularly:

EMR (mandatory and most critical skill)

EC2

Lambda

Candidate should have experience with EMR performance and cost optimization

Overview

Seeking a Data Engineer / Backend Developer to design, build, and operate cloud-native, API-driven Big Data

systems. The role focuses on AWS EMR and Spark-based batch processing with strong emphasis on Python

development, workflow orchestration (Airflow), and production reliability.

Key Responsibilities





• Design and develop API-driven systems to govern, manage, and monitor large-scale batch Big Data

applications.

• Build scalable backend services and data engineering solutions supporting data processing and operational

workflows.

• Develop and maintain data transformation processes using Spark, SQL, Hive, Python, Scala, and related

technologies.

• Build cloud-native data solutions using AWS services including EMR, S3, EC2, Lambda, DynamoDB, and

API Gateway.

• Design and enhance Apache Airflow workflows, including complex DAGs, scheduling, dependency

management, monitoring, and failure handling.

• Optimize AWS EMR performance and cost efficiency across large-scale data workloads.

• Participate in requirements gathering, technical research, and solution design.

• Contribute to architecture reviews, code reviews, performance tuning, and operational readiness.

• Support testing, validation, and integration across

📌 Data Engineer (Hyderabad)
🏢 Arminus
📍 Hyderabad