Position Summary:
We are looking for an enthusiastic entry-level Data Engineer with a growing DevOps mindset to help build and maintain reliable, scalable data pipelines that power business functions across the enterprise. This is a great opportunity for an early-career engineer to learn, grow, and build hands-on expertise — you'll support the team in developing data pipelines, learn CI/CD and operational practices, and take on increasing responsibility under the guidance of senior engineers.
Core Skills:
Databricks · Python (PySpark) · SQL · Data Pipelines · CI/CD
Key Responsibilities:
Engineering & Delivery:
- Help build and maintain data pipelines on Databricks under guidance.
- Develop ETL/ELT processes with attention to data quality, consistency, and scalability.
- Contribute to reusable frameworks for ingestion, transformation, and reconciliation across source systems.
- Follow established engineering standards — coding standards, pipeline patterns, and ETL/ELT best practices.
Operations & DevOps:
- Assist with deploying changes through CI/CD and the Change Request (CR) lifecycle, including validation and ticket closure.
- Participate in problem-solving and root-cause analysis, learning to drive permanent fixes over recurring firefighting.
- Help monitor data workloads and support incident response with guidance from senior engineers.
Collaboration:
- Work with Reporting, Platform, and Business teams to help deliver curated datasets for downstream consumers.
- Communicate progress and issues clearly to engineering peers and mentors.
- Document workflows and runbooks to support reproducibility and knowledge sharing.
What Success Looks Like (First 6–12 Months):
- In your first 6–12 months, you'll build a solid understanding of the data platform, confidently deliver assigned pipeline tasks, and become comfortable with CI/CD and operational practices — with support from senior engineers.
Required Qualifications:
- Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
- 1+ years of experience (including internships) in data engineering or a related area — fresh graduates with relevant internships are encouraged to apply.
- Foundational hands-on knowledge of Databricks, Python (PySpark), and SQL for data processing.
- Exposure to building data pipelines (ETL/ELT), through projects, internships, or coursework.
- Basic understanding of CI/CD pipelines and Git-based version control.
- Familiarity with cloud platforms (AWS preferred) or willingness to learn.
- Awareness of monitoring and observability concepts.
- Good communication skills and eagerness to learn.
Preferred Qualifications:
- Exposure to orchestration frameworks or streaming technologies.
- Basic familiarity with Infrastructure-as-Code and deployment tooling.
- Awareness of observability tooling for data platforms.
- Background or interest in semiconductor manufacturing or large-scale industrial data processing.
- Any Databricks or cloud certification is a plus.
Competencies:
- Eagerness to learn and grow data engineering skills.
- Ownership mindset — takes pride in the quality of assigned work.
- Problem-solving orientation — curiosity and attention to detail.
- Collaboration — works well with peers and mentors across teams.
- Clear communication — able to explain technical details to peers.
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