Data Engineer

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. 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. #J-18808-Ljbffr Saur Energy International