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Key Responsibilities and Required Skills for Junction Builder Assistant (Associate Data Engineer)

💰 $75,000 - $95,000

Data EngineeringSoftware DevelopmentTechnologyEntry-Level

🎯 Role Definition

As a Junction Builder Assistant, you are a pivotal member of our Data Engineering team, serving as the foundational support for creating and maintaining the "junctions" that connect our vast data ecosystem. This entry-level role is perfect for a passionate and detail-oriented individual eager to launch their career in data engineering. You will work alongside senior engineers to build, manage, and optimize the ETL/ELT data pipelines that are the lifeblood of our company's analytics and business intelligence initiatives. Your work will directly impact our ability to make data-driven decisions by ensuring the timely, accurate, and secure flow of information across all departments.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst or Business Intelligence Analyst
  • IT Support Specialist or Database Administrator
  • Recent Graduate (Computer Science, Engineering, or STEM fields)

Advancement To:

  • Data Engineer
  • Senior Data Engineer
  • Data Architect
  • Analytics Engineer

Lateral Moves:

  • Advanced Data Analyst
  • Business Intelligence (BI) Developer
  • DevOps Engineer

Core Responsibilities

Primary Functions

  • Assist in the end-to-end development, implementation, and maintenance of robust, scalable, and efficient ETL/ELT data pipelines.
  • Develop, test, and optimize complex SQL queries to perform data extraction, transformation, and aggregation from diverse source systems.
  • Write clean, maintainable, and well-documented code, primarily in Python or Scala, for data processing and workflow automation tasks.
  • Collaborate closely with senior data engineers to translate business requirements and data models into functional technical specifications.
  • Perform data profiling and quality checks to identify anomalies, inconsistencies, and missing data, implementing rules to ensure high data integrity.
  • Support the management and administration of our cloud data warehouse environment (e.g., Snowflake, BigQuery, Redshift), including schema management and access control.
  • Implement comprehensive monitoring and alerting for data pipelines to ensure timely detection and resolution of job failures or performance degradation.
  • Participate in troubleshooting and debugging data-related issues, working methodically to identify root causes and implement effective solutions.
  • Contribute to the continuous improvement of our data engineering standards, tooling, and best practices under the guidance of senior team members.
  • Manage and orchestrate data workflows using tools like Apache Airflow, Prefect, or Dagster to ensure reliable and timely data delivery.
  • Document data sources, pipeline logic, and transformation rules meticulously to create a clear and accessible knowledge base for the team and stakeholders.
  • Gain hands-on experience with cloud data services on platforms like AWS (S3, Glue, Lambda), Azure (Data Factory, Synapse), or GCP (Cloud Storage, Dataflow).
  • Assist in migrating legacy data processes to modern, cloud-native data platforms and architectures.
  • Utilize version control systems like Git to manage code and collaborate effectively within a team-based development environment.
  • Support the integration of new data sources, including third-party APIs, streaming data, and unstructured data, into our central data platform.
  • Conduct performance tuning of data pipelines and database queries to minimize latency and optimize resource consumption.
  • Engage in peer code reviews to learn from others, share knowledge, and maintain high standards of code quality across the team.
  • Help build and maintain foundational data models that are optimized for analytical querying and reporting purposes.
  • Ensure all data handling processes are compliant with data governance policies and security standards, such as GDPR and CCPA.
  • Work with data analysts and business intelligence teams to understand their data requirements and provide the necessary datasets for their analysis.
  • Automate manual data-related tasks to improve operational efficiency and reduce the potential for human error.
  • Participate in the evaluation and proof-of-concept for new data technologies and tools that could enhance our data infrastructure.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis from various business units.
  • Contribute to the organization's broader data strategy and technology roadmap discussions.
  • Collaborate with business units to translate ambiguous data needs into concrete engineering requirements.
  • Participate actively in sprint planning, daily stand-ups, and other agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • SQL Proficiency: Strong ability to write complex, optimized SQL queries for data manipulation (DML), definition (DDL), and querying across different database systems.
  • Programming Fundamentals: Solid understanding of a programming language like Python, Java, or Scala, with a focus on data structures and algorithms.
  • ETL/ELT Concepts: Foundational knowledge of Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) principles and modern data stack architectures.
  • Cloud Platform Exposure: Familiarity with at least one major cloud provider (AWS, Azure, or GCP) and its core data services (e.g., S3, Blob Storage, Glue, Data Factory).
  • Database Knowledge: Understanding of both relational (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, DynamoDB) database concepts.
  • Version Control: Experience using Git for source code management, including branching, merging, and pull requests in a collaborative setting.
  • Data Warehousing Concepts: Basic knowledge of data warehousing principles, including star/snowflake schemas and dimensional modeling.

Soft Skills

  • Analytical Problem-Solving: A logical and systematic approach to identifying, analyzing, and resolving complex technical challenges.
  • Strong Communication: Ability to clearly articulate technical concepts and findings to both technical and non-technical audiences, both verbally and in writing.
  • Eagerness to Learn: A proactive and curious mindset with a strong desire to master new technologies, tools, and data engineering best practices.
  • Attention to Detail: Meticulous and thorough in your work, especially concerning data quality, code accuracy, and technical documentation.
  • Collaborative Spirit: A team player who thrives in a collaborative environment, open to giving and receiving constructive feedback to foster collective growth.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in a quantitative or technical field, or equivalent practical work experience.

Preferred Education:

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

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Science / Statistics / Mathematics
  • Information Technology / Management Information Systems

Experience Requirements

Typical Experience Range: 0-2 years of experience in a data-related role (including internships or co-op positions).

Preferred: Prior internship experience in data engineering, software development, or data analysis is highly desirable. A portfolio of personal or academic projects involving data processing, databases, or API integration will be viewed favorably.

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