Website IBM

IBM Data Engineer – Software Group (Bangalore, India)

Opportunity Overview

Attribute Details
Organisation IBM Software
Role Title Data Engineer
Job Location Bangalore, Karnataka, India
Domain Focus Data Engineering, ETL/ELT Pipelines, Real-Time Streaming, Cloud Infrastructure
Experience Level Mid-Level (2 to 4 Years, including relevant internships)
Education Requirement Bachelor’s Degree in Computer Science, IT, or related field (Master’s preferred)
Application Fee Free

Role Scope & Technical Architecture

The Data Engineer within IBM Software designs, builds, and maintains high-throughput data infrastructure, delivering reliable internal data products and maintaining operational SLAs:

[Raw Enterprise Data Sources] ➔ [ETL / ELT Processing & Airflow Orchestration] ➔ [GCP Cloud Infrastructure & Storage] ➔ [Real-Time Streams & Analytical Data Warehouses]

Core Responsibilities

  • Pipeline Architecture: Design, construct, and deploy reliable data pipelines transferring batch and real-time data across enterprise platforms and Data Warehouses.

  • SLA & Performance Governance: Manage pipeline SLAs, ensure high data availability, optimize query performance, and monitor system health.

  • Cloud Infrastructure Management: Maintain GCP cloud resources, including IAM access controls, service accounts, and managed cloud services.

  • Cross-Functional Collaboration: Partner with Data Scientists, Data Analysts, and business stakeholders to build internal data products driving operational efficiency.

Candidate Eligibility & Technical Skills Matrix

Minimum Qualifications

  • Experience: 2 to 4 years of professional experience in data engineering (including technology internship experience).

  • Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, or equivalent practical experience.

  • Data Fundamentals: Strong knowledge of data modeling, ETL/ELT methodologies, data structures, and analytical problem-solving.

Technical Skills Matrix

Category Skill Domain & Focus Areas
Pipeline & Orchestration

• Orchestration: Apache Airflow (DAG design, task scheduling, operator customization)


• Architectures: ETL/ELT pipelines, event-driven & real-time streaming processing
Cloud Infrastructure (GCP)

• Google Cloud Platform services (BigQuery, Cloud Storage, Dataflow, Pub/Sub)


• Cloud Security: Identity & Access Management (IAM), Service Account management
Data Quality & Modeling

• Data modeling concepts (Dimensional, Star/Snowflake schemas)


• Data integrity enforcement, pipeline monitoring, data cleansing, and error handling
Communication & Agile

• Clear technical communication (verbal and written)


• Structured analytical thinking and root-cause analysis for data anomalies

Selection Workflow & Evaluation Stages

[Resume & Eligibility Screening] ➔ [Technical Assessment / Screening Call] ➔ [Data Engineering Technical Loop] ➔ [Managerial & Cultural Fit Round]

  1. Resume Screening: Evaluation of 2–4 years of data engineering experience, hands-on Apache Airflow exposure, and cloud computing background.

  2. Technical Screening: Assessment of core data structures, SQL proficiency, ETL/ELT concepts, and basic Python/data processing logic.

  3. Data Engineering Deep Dive:

    • Pipeline Design & SQL: Scenario-based query optimization, data warehouse schema design, and data troubleshooting.

    • Orchestration & Cloud Infrastructure: Deep dive into Apache Airflow DAG management, real-time architecture, and GCP IAM/resource setup.

  4. Managerial & Stakeholder Discussion: Evaluation of cross-functional communication, SLA management experience, and alignment with IBM Software engineering practices.

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