Data Engineer

Descripción del puesto:

Be a part of a revolutionary change
At PMI, we've chosen to do something incredible. We're totally transforming our business and building our future on one clear purpose - to deliver a smoke-free future.
With huge change, comes huge opportunity. So, wherever you join us, you'll enjoy the freedom to dream up and deliver better, brighter solutions and the space to move your career forward in endlessly different directions.

Role Summary

The Junior Data Engineer (Data Insights & Analytics) supports the design, build, testing, and maintenance of analytics-ready data products under the guidance of senior engineers, data architects, or technical leads.
This role helps ensure that data pipelines, models, and curated datasets reliably support business insights, dashboards, and analytics use cases, while following established engineering standards, documentation practices, and delivery processes.
The role is hands-on and execution-oriented, focused on developing technical depth, understanding business logic, improving data quality, and contributing to predictable delivery outcomes within the engineering team.

Primary Purpose

* Support the delivery of trusted, high-quality data products through reliable and well-documented data engineering work.
* Translate approved requirements and specifications into data transformations, pipeline components, and analytical datasets with guidance from senior team members.
* Contribute to the maintenance, testing, and continuous improvement of data pipelines, analytical models, and semantic layers.
* Support technology upgrades, migrations, and platform improvements by executing assigned technical activities and validating outcomes.
Your day to day:

Data Engineering Execution

* Build, enhance, and maintain analytics-focused data pipelines, including ingestion, transformation, curated layers, and data marts.
* Develop ETL/ELT logic based on approved designs, user stories, and technical specifications.
* Support the implementation of analytical models and semantic layers that enable dashboards, reporting, and advanced analytics.
* Follow coding, modeling, naming, and documentation standards defined by the data architecture and engineering teams.

2. Data Quality, Testing & Reliability

* Execute data validation, reconciliation, and quality checks to ensure outputs are accurate and explainable.
* Support testing activities, including unit testing, regression testing, and validation of business rules.
* Investigate data issues, document findings, and support root-cause analysis with guidance from senior engineers.
* Monitor assigned pipelines or datasets and escalate risks related to data availability, quality, performance, or freshness.

3. Delivery & Collaboration

* Work with senior engineers, data architects, delivery managers, BI teams, and analytics stakeholders to understand requirements and technical expectations.
* Provide timely updates on assigned tasks, dependencies, blockers, and estimated completion dates.
* Contribute to backlog execution by completing assigned user stories, technical tasks, defect fixes, and documentation items.
* Participate in team ceremonies, design discussions, code reviews, and knowledge-sharing sessions.

4. Documentation & Standards

* Document data transformations, assumptions, source-to-target mappings, and key business logic in a clear and reusable way.
* Maintain technical documentation for assigned pipelines, datasets, and analytical models.
* Apply established development practices, including version control, peer review, testing evidence, and deployment documentation.
* Support data discoverability by keeping metadata and dataset descriptions accurate and up to date.

5. Continuous Improvement & Learning

* Identify opportunities to improve pipeline performance, reliability, reusability, or analyst productivity within assigned scope.
* Learn and apply modern data engineering practices, tools, and platform capabilities used by the team.
* Seek feedback, build technical autonomy, and progressively take ownership of more complex engineering tasks.
* Contribute to a collaborative engineering culture by sharing learnings, reusable components, and lessons learned.
Who we re looking for:
* 2-3 years of hands-on experience in data engineering, analytics engineering, BI engineering, or related data roles.
* Practical experience with SQL and data transformation logic; familiarity with ETL/ELT concepts and data modeling basics.
* Exposure to cloud data platforms, data warehouses, orchestration tools, or modern analytics platforms is preferred.
* Basic understanding of data quality, testing, reconciliation, performance tuning, and documentation practices.
* Ability to collaborate with technical and non-technical stakeholders, communicate progress clearly, and escalate blockers early.
* Growth mindset, attention to detail, curiosity, and willingness to learn from senior engineers and architects.
* Assigned tasks are delivered on time, with good quality, clear documentation, and limited rework.
* Data pipelines and analytical datasets are reliable, tested, and aligned with agreed business logic and KPI definitions.
* The engineer becomes increasingly autonomous, while knowing when to ask questions or escalate risks.
* Technical deliverables follow team standards and contribute to reusable, supportable data products.
* Business and analytics teams can trust and use the data outputs supported by the role