Data Engineer ; Onsite

The IT Data Engineer I builds foundational skills in SQL, Python, and data pipeline development. Working under direct supervision, this role supports data ingestion, transformation, and quality processes while developing awareness of cloud platforms, data governance, and AI-assisted development tools.

• Deliver low‑complexity data engineering tasks as part of a team.
• Work under direct supervision; receive instructions, guidance, and direction from others.
• Scope:
  Team.
• Develop and maintain SQL-based data pipelines, ensuring efficient ETL processes.
• Write Python scripts to support data transformation, ingestion, and automation tasks.
• Develop foundational skills in working with common data serialization and interchange formats (e.g., JSON, CSV, Parquet, Avro) for data ingestion and transformation.
• Learn and apply foundational concepts of data warehousing, data architecture, and basic data modeling (e.g., fact/dimension tables, normalization).
• Assist in applying data classification standards to datasets and pipelines under guidance, learning to identify data sensitivity levels (e.g., public, internal, confidential, restricted).
• Provide assistance in data governance, data quality, and data privacy activities, including following PII handling protocols and data retention policies.
• Escalate potential data exposure or classification issues to senior engineers.
• Use version control systems (e.g., Git) for all code and configuration changes, including branching, committing, and participating in pull requests.
• Gain foundational exposure to major cloud platforms (e.g., AWS, GCP, or Azure) and understand how data services operate in a cloud environment.
• Develop basic awareness of containerization concepts and how pipelines are packaged and deployed.
• Learn basic data testing and validation practices using data quality frameworks to ensure pipeline output quality.
• Assist in implementing basic logging and monitoring for data pipelines to support troubleshooting and operational visibility.
• Collaborate with team members and stakeholders to understand requirements for data solutions.

• Develop a foundational understanding of AI/ML concepts and their application to data pipelines and automation.
• Learn to use AI-assisted tools and copilots to accelerate data engineering tasks such as code generation, debugging, and documentation.
• Gain exposure to prompt engineering fundamentals and how large language models (LLMs) can support data operations.
• Understand that AI-generated outputs can be inaccurate; learn to validate and verify AI-produced code, queries, and results before use.

Education:

Bachelor's Degree or relevant experience.

Preferred Certification(s):
Any relevant IT Certification (e.g., AWS Cloud Practitioner, Google Cloud Digital Leader, Azure Fundamentals, Databricks Lakehouse Fundamentals).

Experience:

Less than one year of relevant or practical experience.

• Proficiency in SQL and foundational Python scripting.
• Understanding of data warehousing concepts, basic data modeling, and basic data architecture.
• Familiarity with common data serialization and interchange formats.
• Foundational exposure to modern cloud data platforms and at least one major cloud platform (e.g., AWS, GCP, or Azure).
• Basic proficiency with version control systems for collaborative development.
• Basic awareness of AI/ML concepts and AI‑assisted development tools for code generation and data analysis.

• Curiosity:
  Eagerness to explore and understand new data concepts, AI technologies, and emerging patterns.
• Receptiveness to Feedback:
  Willingness to accept and act on coaching from senior engineers.
• Attention to Detail:
  Meticulous approach to data handling and…