Key Responsibilities and Required Skills for a Junction Trainee (Data & Analytics)

🎯 Role Definition

Welcome to the Junction Trainee role, a foundational position designed specifically for emerging talent passionate about the world of data. This isn't just a job; it's the start of a career. As a Junction Trainee, you'll be positioned at the unique intersection—or "junction"—of data engineering, data analytics, and business strategy.

This rotational and development-focused role is perfect for individuals who are curious, technically inclined, and eager to understand the full lifecycle of data. You will learn how to build the infrastructure that moves data, how to analyze that data to find meaning, and how to present those findings to drive real business decisions. You'll be mentored by senior professionals and given hands-on experience with the tools and technologies that power a modern data organization.

📈 Career Progression

Typical Career Path

Entry Point From:

Recent university graduates (Bachelor's or Master's) in quantitative or technical fields.
Career changers with a demonstrated passion and foundational knowledge in data.
Graduates of data science or data engineering bootcamps.

Advancement To:

Data Engineer
Data Analyst / Analytics Engineer
Business Intelligence (BI) Developer

Lateral Moves:

Product Analyst
Data Governance Specialist

Core Responsibilities

Primary Functions

Assist in the design, development, and maintenance of robust, scalable ETL/ELT data pipelines to ingest data from a wide variety of internal and external sources.
Work under the guidance of senior engineers to implement complex data transformations using SQL and Python to prepare and clean datasets for analytical and reporting purposes.
Contribute to the management and optimization of our cloud-based data warehouse (e.g., Snowflake, BigQuery, Redshift), ensuring data is stored efficiently and is readily accessible.
Develop a strong, practical understanding of data modeling principles and apply them to help create logical and physical data models that support evolving business requirements.
Perform rigorous data quality checks and implement automated validation scripts to ensure the accuracy, completeness, and consistency of data across all of our systems.
Build and automate foundational reports and interactive dashboards using modern BI tools like Tableau or Power BI to provide actionable insights to business stakeholders.
Write and meticulously optimize complex SQL queries to conduct in-depth data extraction and analysis for both planned projects and urgent ad-hoc stakeholder requests.
Utilize version control systems like Git to manage code, participate in peer code reviews, and collaborate effectively within an agile development environment.
Meticulously document data pipelines, data models, and critical technical processes to create a shared understanding and facilitate future maintenance and development.
Partner directly with data analysts and business users to deeply understand their data requirements and translate those needs into clear technical specifications for the engineering team.
Proactively monitor the performance of data processing jobs and pipelines, troubleshooting failures and collaborating with the team to resolve issues in a timely and efficient manner.
Participate actively in the full software development lifecycle, from requirements gathering and design sprints to testing, deployment, and ongoing operational support.
Learn and apply industry best practices in data governance, data security, and privacy (e.g., GDPR, CCPA) to ensure all our data handling is compliant and secure.
Engage in exploratory data analysis using Python libraries such as Pandas, NumPy, and Matplotlib to uncover hidden trends, patterns, and anomalies in large, complex datasets.
Support the migration of legacy data systems to modern, cloud-native data platforms, assisting with critical data mapping, validation, and testing efforts.
Create and diligently maintain technical documentation for key data sources, business metrics, and transformation logic to ensure a single source of truth for the organization.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis to answer pressing business questions from various departments.
Contribute to the organization's broader data strategy and roadmap by providing fresh perspectives and researching emerging technologies.
Collaborate with various business units, including marketing, finance, and product, to translate their data needs into actionable engineering requirements.
Participate in all sprint planning, daily stand-ups, and retrospective agile ceremonies within the data engineering team to ensure alignment and continuous improvement.

Required Skills & Competencies

Hard Skills (Technical)

Strong proficiency in SQL, with the ability to write complex queries, joins, and subqueries for data manipulation and analysis.
Foundational programming skills in Python, particularly with experience or exposure to data-focused libraries like Pandas and NumPy.
A solid theoretical understanding of data warehousing concepts, including dimensional modeling, star/snowflake schemas, and SCDs (Slowly Changing Dimensions).
Foundational knowledge of at least one major cloud platform (AWS, Google Cloud, or Azure) and familiarity with its core data services (e.g., S3, EC2, Blob Storage, GCS).
Exposure to data visualization and Business Intelligence tools such as Tableau, Power BI, Looker, or similar platforms.
A clear understanding of ETL/ELT principles and the conceptual role of data pipeline orchestration tools like Apache Airflow.
Practical experience using version control systems, primarily Git and GitHub/GitLab, for code management and collaboration.

Soft Skills

Exceptional analytical and problem-solving abilities, with a meticulous, detail-oriented mindset.
Strong verbal and written communication skills, with a natural ability to explain complex technical ideas to non-technical stakeholders.
A relentless curiosity and a demonstrable passion for learning new technologies, tools, and data-driven concepts.
A highly collaborative, team-player attitude with a professional demeanor and a willingness to both learn from and contribute to the team's success.
High degree of adaptability and resilience in a fast-paced, dynamic, and evolving technical landscape.

Education & Experience

Educational Background

Minimum Education:

Bachelor's Degree in a relevant field.

Preferred Education:

Master's Degree or specialized post-graduate certifications in data science, analytics, or engineering.

Relevant Fields of Study:

Computer Science, Engineering, or a related technical field.
Statistics, Mathematics, Economics, or another quantitative discipline.

Experience Requirements

Typical Experience Range: 0-2 years of relevant experience.

Preferred: Internship, co-op, or significant academic project experience in a data-related field (data analysis, software engineering, or business intelligence) is highly regarded.