Data Engineer

Key Skills

AWS, Azure, Continuous Integration & Continuous Delivery - CI/CD, Data Analytics, Data Engineering, Data Modeling, Data science techniques, Data Warehousing, Database, Databricks, Design, DevOps, Effective communication skills, Employee life cycle, Google Cloud Platform (GCP), Java Programming, PowerBI, Python Programming, Scala Programming, SQL

Job Description

Data & Analytics is a team of enthusiastic and bright people who enable quicker & smarter business decisions while ensuring a delightful user experienceacross the enterprise. Placed Centrally, D&A Champions have access to a pool of talented data experts spread across six continents and work with ourpartners, functions, categories, and geographies to harmonise decision-making and deliver technology solutions to drive business insight.

The D&A Data Engineering and Architecture team in Brazil, is the engine behind the ambition of making Data Engineering and Architecture knowledge a

common practice. This team is responsible for ensuring Unilever Data Lake is modelled as per industry best standards. The services from this team are

consumed by the Data SMEs, Product teams, various data factories for products rollout and the Data Governance team.

We are looking at enthusiastic and bright candidates to become a part of this team and work closely with our partners and stakeholders to deliver superior

analytical capabilities.

Job Purpose

As a Data Engineer, you will play a pivotal role in designing, building, and maintaining our data infrastructure and systems, ensuring the availability, efficiency, and scalability of our data pipeline. You will collaborate closely with our data scientists, analysts, and software engineers to support data-driven initiatives and drive innovation in our data management processes.

Responsibilities:

1. Design, develop, and implement scalable and efficient data pipelines, ETL processes, and data integration workflows to collect, process, and store large volumes of structured and unstructured data.
2. Collaborate with cross-functional teams, including data scientists, analysts, and software engineers, to understand data requirements, identify bottlenecks, and optimize data infrastructure for performance and reliability.
3. Build and maintain data systems, databases, and data warehouses, ensuring data quality, integrity, and security throughout the entire data lifecycle.
4. Design and implement relational 3NF & dimensional: Graph & other non-relational data models and schemas to support diverse analytical and reporting needs.
5. Develop streamlined & automated engineering solutions based on metadata leveragingappropriate tools and technologies.
6. Develop processes that are stable and with high quality error trapping & messaging such that operational support in efficient & low-cost.
7. Collaborate with stakeholders at all levels to understand their data needs & communicate clear & concise solutions for both projects & ad-hoc based data requirements.
8. Stay up to date with emerging trends and technologies in data engineering, recommending and implementing improvements to enhance data processing efficiency, scalability, and reliability.
9. Implement data governance practices and ensure compliance with architectural, process, integration & data privacy and security policies, standards & regulations.
10. Document processes, systems, and workflows to ensure knowledge sharing and maintainable code. Wherever possible linking & reusing existing specification & process documentation as metadata.

Qualifications:

1. Bachelor’s degree in computer science, Engineering, or a related field or equivalent experience.
2. Proven experience as a Data Engineer, Data Modeler, Data Scientist or in a similar role, working with large-scale data systems and ETL processes.
3. Strong SQL programming experience.
4. Proven programming experience in data management languages, for example: Python, Java, or Scala or equivalent.
5. Proficiency in designing and implementing data pipelines using tools and frameworks such as Apache Spark, Databricks or similar technologies.
6. Solid understanding of data modelling & data warehousing concepts and experience with database systems.
7. Familiarity with cloud-based data platforms such as Azure, AWS, or Google Cloud Platform, and knowledge of related services like AWS Glue, Azure Data Factory, or Google Cloud Dataflow. (Azure Preferred)
8. Knowledge of data visualization tools and frameworks such as Power BI, Tableau, or similar tools, would be a plus.
9. Strong problem-solving and analytical skills, with the ability to troubleshoot and resolve data-related issues.
10. Excellent communication and collaboration skills, with the ability to work effectively in a team-oriented environment.
11. Fair understanding of DevOps & CI/CD Deployment Process.
12. Strong attention to detail, with a focus on data quality, integrity, and security.

What we offer:

Rewards and benefits are just one part of the reason Unilever is a great place to work. We offer significant benefits, including a strong bonus, healthcare options, company share scheme, professional membership support and much more. In addition, there are a wide range of options around flexible working for the future.

Diversity at Unilever is about inclusion, embracing differences, creating possibilities and growing together for better business performance. We embrace diversity in our workforce. This means giving full and fair consideration to all applicants and continuing development of all employees regardless of age, disability, gender reassignment, race, religion or belief, sex, sexual orientation, marriage and civil partnership, and pregnancy and maternity.