Fujitsu: Junior Data Engineer

Job Description

The Junior Data Engineer role at Fujitsu in Pune is an early-career opportunity for candidates who want to build practical experience in data engineering, production support, ETL/ELT processes, and enterprise data operations. The position focuses on supporting the day-to-day functioning of data pipelines and applications through monitoring, first-level troubleshooting, data validation, incident management, and operational documentation. Working under the guidance of intermediate and senior engineers, the Junior Data Engineer will monitor scheduled pipeline executions, investigate basic job failures, verify data loads, check alerts, and ensure that operational activities are properly recorded. The role is particularly suitable for candidates with foundational knowledge of SQL and data engineering who want exposure to real-world production environments and enterprise data platforms.

A core part of the position involves ensuring that data engineering workflows operate reliably and that potential problems are identified quickly. The engineer will perform first-level checks when jobs fail, files are missing, data loads are incomplete, or source and target information does not match. Basic SQL will be used to inspect datasets, validate job outputs, perform data checks, and support source-to-target reconciliation activities. The role also involves working with ServiceNow to create, update, and track incidents, service requests, tasks, and change requests. When an issue requires deeper technical investigation, the engineer will collect relevant logs, screenshots, error messages, and supporting information before escalating the problem to senior team members. This structured approach helps maintain reliable data operations while reducing unnecessary delays in issue resolution.

The opportunity also provides a strong foundation for developing broader data engineering and cloud skills. Junior engineers may gain exposure to file-based ingestion, SFTP, AWS S3, AWS Glue, Snowflake, Databricks, Python, and monitoring platforms such as CloudWatch, depending on project requirements. Documentation is another important responsibility, with engineers contributing to runbooks, standard operating procedures, operational checklists, knowledge-transfer material, and daily status reports. Since the role operates within an enterprise production-support environment, attention to detail, process discipline, communication, and willingness to learn are essential. For graduates and early-career professionals, this Fujitsu position can provide valuable experience in data pipeline monitoring, cloud data technologies, incident management, SQL, production support, and the operational practices required to build a long-term career in data engineering.

Roles & Responsibilities

1. Monitor Data Pipelines
   Monitor scheduled data pipeline runs, job statuses, alerts, file arrivals, and data refresh activities to identify operational issues at an early stage.
2. Perform First-Level Troubleshooting
   Investigate basic pipeline failures, missing files, incomplete data loads, data mismatches, and system alerts using established troubleshooting procedures and available operational documentation.
3. Validate Data Outputs
   Run basic SQL queries and perform data-quality checks to validate job outputs, confirm expected records, and support source-to-target reconciliation activities.
4. Manage ServiceNow Tickets
   Create, update, and track incidents, service requests, tasks, and change requests in ServiceNow while ensuring that ticket information remains accurate and complete.
5. Collect Technical Evidence
   Gather logs, screenshots, error messages, job details, timestamps, and other relevant information before escalating complex technical issues to intermediate or senior engineers.
6. Support Production Operations
   Assist with routine production-support activities, monitoring reports, operational checks, and scheduled tasks while following established procedures and supervision requirements.
7. Maintain Operational Documentation
   Contribute to runbooks, standard operating procedures, operational checklists, knowledge-transfer documents, and other materials required for consistent data operations.
8. Track Data and Job Issues
   Identify recurring pipeline problems, record operational observations, and communicate relevant information to senior team members to support effective issue resolution.
9. Prepare Operational Reports
   Assist with daily status updates, monitoring reports, operational metrics, and other reporting activities that provide visibility into data pipeline health and production performance.
10. Support Incident Management
    Follow defined incident-management processes, maintain accurate records of issues, and ensure that problems are escalated appropriately according to operational priorities and procedures.
11. Assist with Data Engineering Activities
    Support experienced engineers with basic ETL/ELT, data ingestion, validation, and pipeline-related activities while developing practical knowledge of enterprise data environments.
12. Develop Technical Capabilities
    Continuously improve knowledge of SQL, Python, cloud platforms, data engineering tools, monitoring systems, and production-support practices through hands-on learning and team guidance.

Requirements & Eligibility

1. SQL Knowledge
   Candidates should have a basic understanding of SQL and be comfortable writing simple queries for data validation, troubleshooting, record checking, and source-to-target comparisons.
2. ETL/ELT Fundamentals
   Applicants should understand basic ETL and ELT concepts, including how data is extracted, transformed, loaded, and processed through enterprise data pipelines.
3. Data Validation Skills
   Basic knowledge of data validation and reconciliation is important for checking whether pipeline outputs are complete, accurate, consistent, and aligned with expected results.
4. Pipeline Monitoring
   Candidates should understand the fundamentals of monitoring scheduled jobs, identifying alerts, checking job status, verifying file arrivals, and recognizing common pipeline failures.
5. Incident Management
   Familiarity with incident-management processes is beneficial, including documenting problems, updating tickets, collecting troubleshooting information, escalating issues, and tracking resolution progress.
6. ServiceNow Knowledge
   Experience or awareness of ServiceNow is valuable because the position involves creating and maintaining incidents, service requests, tasks, and change-related records.
7. Production Support Awareness
   Candidates should understand basic production-support principles and be comfortable following operational procedures, checklists, escalation processes, and service-management practices.
8. Cloud and Data Platform Exposure
   Familiarity with AWS S3, AWS Glue, Snowflake, Databricks, or similar cloud data technologies is advantageous, although the supplied role emphasizes foundational knowledge and willingness to learn.
9. Programming and Monitoring Skills
   Basic Python knowledge and awareness of CloudWatch or equivalent monitoring tools can strengthen a candidate's profile and provide a foundation for future data engineering responsibilities.
10. Communication and Learning Ability
    Candidates should demonstrate clear communication, attention to detail, documentation skills, teamwork, adaptability, and a willingness to learn from senior engineers in a structured enterprise environment.

Expected Salary

For a Junior Data Engineer in Pune, a realistic entry-level market estimate is approximately ₹3.5 LPA–₹7 LPA, depending on SQL proficiency, data engineering fundamentals, cloud knowledge, Python skills, academic background, and relevant internships or projects. Candidates with stronger AWS, ETL, Snowflake, Databricks, and production-support capabilities may have opportunities toward the upper end of the range.

The supplied Fujitsu posting does not disclose an official salary range, so this figure should be considered an indicative market estimate rather than confirmed Fujitsu compensation. The final package can vary based on experience, project requirements, internal grade, technical assessment, and the candidate's overall profile.

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