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Key Responsibilities and Required Skills for a Junction Engineer Assistant

💰 $55,000 - $85,000

Data EngineeringTechnologyAnalyticsIT

🎯 Role Definition

The Junction Engineer Assistant is a vital, entry-level role within the data organization, acting as the primary support for the entire data engineering function. This individual is instrumental in the day-to-day operations of the company's data ecosystem, focusing on the maintenance, monitoring, and optimization of data pipelines and systems. Working under the guidance of senior engineers, the Assistant helps ensure that clean, reliable, and timely data is available for analysts, data scientists, and business stakeholders. This role is a fantastic launchpad for a career in data engineering, offering hands-on experience with foundational technologies and processes that turn raw data into actionable insights.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Recent Graduate (Computer Science, IT, Engineering)
  • Data Analyst looking for a more technical path
  • IT Support or Database Administration roles
  • Business Intelligence Intern

Advancement To:

  • Data Engineer
  • Analytics Engineer
  • BI (Business Intelligence) Developer
  • Cloud Data Engineer

Lateral Moves:

  • Data Scientist (with further education/training)
  • Database Administrator (DBA)
  • Software Engineer (Data-focused)

Core Responsibilities

Primary Functions

  • Assist in the design, construction, and ongoing maintenance of robust, scalable data pipelines to ingest and process information from a wide variety of sources.
  • Support the development and implementation of ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes to convert raw data into a clean, structured format suitable for analysis.
  • Perform routine data quality checks, audits, and validation procedures to ensure the accuracy, completeness, and integrity of data within our warehousing solutions.
  • Help troubleshoot and resolve issues with data pipelines, processing jobs, and data warehouse performance to minimize downtime and impact on business users.
  • Write, maintain, and optimize SQL queries of varying complexity to perform data extraction, transformation, and loading tasks as directed by senior staff.
  • Collaborate with senior engineers to build and manage data models that effectively support business intelligence tools, dashboards, and analytics initiatives.
  • Meticulously document data sources, data lineage, transformation logic, and pipeline architecture to create a clear and accessible knowledge base for the team.
  • Monitor the performance and cost of data infrastructure, identifying and suggesting optimizations for improved efficiency under supervision.
  • Assist in the management and administration of cloud-based data platforms such as AWS (S3, Redshift), Azure (Data Lake, Synapse), or GCP (BigQuery, Cloud Storage).
  • Support the integration of new data sources and third-party tools into the existing data ecosystem, ensuring seamless data flow.
  • Execute data migration projects under the close guidance of senior team members, including testing and validation of migrated data.
  • Develop and maintain scripts, primarily in Python, for data manipulation, process automation, and scheduling of routine tasks.
  • Help maintain version control for all data engineering code and artifacts using systems like Git, participating in branching and merging workflows.
  • Participate actively in code reviews to learn best practices in software engineering and contribute to the overall quality of the team's codebase.
  • Provide first-level support to data analysts and business users who have questions or issues related to data access, availability, and definitions.
  • Assist in the creation and upkeep of internal dashboards and reports used for monitoring data pipeline health, job success rates, and system performance.
  • Contribute to the implementation of data governance and security policies to ensure that sensitive data is handled responsibly and in compliance with regulations.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to assist business stakeholders with urgent needs.
  • Contribute to the organization's broader data strategy and roadmap by providing feedback from a ground-level operational perspective.
  • Collaborate with business units to help translate their data needs and questions into tangible engineering requirements for new projects.
  • Participate in sprint planning, daily stand-ups, and other agile ceremonies within the data engineering team.
  • Stay informed about emerging trends, new tools, and best practices within the fast-evolving data engineering field.
  • Assist in preparing clear documentation and presentations for both technical and non-technical audiences.

Required Skills & Competencies

Hard Skills (Technical)

  • SQL Proficiency: Strong ability to write and understand complex SQL queries, including joins, subqueries, and window functions, for data manipulation and analysis.
  • Foundational Programming: Basic to intermediate programming skills in a language like Python (preferred) or Java, with a focus on scripting, automation, and data manipulation libraries (e.g., Pandas).
  • Data Warehousing Concepts: A solid understanding of the principles of data warehousing, including dimensional modeling concepts like star schemas, facts, and dimensions.
  • Cloud Platform Exposure: Familiarity with at least one major cloud provider's data services, such as AWS (S3, Redshift), Azure (Blob Storage, Synapse), or Google Cloud (GCS, BigQuery).
  • ETL/ELT Principles: A theoretical or practical understanding of ETL/ELT workflows and exposure to data integration tools (e.g., dbt, Airflow, Fivetran, SSIS).
  • Database Knowledge: Familiarity with both relational (e.g., PostgreSQL, MySQL) and, ideally, NoSQL (e.g., MongoDB) database systems.
  • Version Control: Experience using Git for source code management, including basic commands for cloning, committing, pushing, and pulling.

Soft Skills

  • Problem-Solving & Detail-Orientation: A methodical approach to troubleshooting issues with a keen eye for detail and a commitment to accuracy.
  • Communication Skills: The ability to clearly articulate technical concepts and problems to both technical peers and non-technical business users.
  • Eagerness to Learn: A proactive and curious mindset, with a strong desire to learn from senior team members and continuously develop technical skills.
  • Collaboration & Teamwork: A cooperative spirit and the ability to work effectively within a team, contributing to shared goals and projects.
  • Organizational Skills: Excellent time management and the ability to prioritize tasks effectively when supporting multiple requests and projects simultaneously.

Education & Experience

Educational Background

Minimum Education:

  • A Bachelor's degree in a relevant technical field or equivalent, demonstrable practical experience through projects or certifications.

Preferred Education:

  • A Bachelor's or Master's degree in Computer Science, Information Systems, or a related quantitative discipline.

Relevant Fields of Study:

  • Computer Science
  • Information Technology
  • Data Science / Analytics
  • Engineering
  • Statistics

Experience Requirements

Typical Experience Range:

  • 0-2 years of experience in a data-related role. Internships, co-op programs, and significant academic projects are highly relevant.

Preferred:

  • Demonstrable experience building data-related projects (personal, academic, or professional). A portfolio on GitHub showcasing SQL, Python scripting, or simple data pipelines is highly advantageous.
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