Data Engineer Associate

Description

Summary: The Data Engineer (Associate) designs, builds, and maintains data pipelines and analytics-ready datasets, collaborating with stakeholders to deliver trusted data assets. Highlights: 1. Design, build, and maintain robust global data platforms and pipelines. 2. Collaborate with analytics, data science, and business stakeholders. 3. Implement data transformations and analytical models for curated datasets. Job Summary: Data \& Analytics function is dedicated to designing and delivering robust global data platforms that enable data ops, business solutions and high\-quality analytics. The Data Engineer (Associate) designs, builds, and maintains data pipelines and analytics\-ready datasets that power reporting, advanced analytics, and data products. This role focuses on implementing well\-defined ingestion and transformation patterns, ensuring data quality and reliability, and collaborating closely with analytics, data science, and business stakeholders to deliver trusted data assets. Responsibilities: * Design, build, and maintain batch and/or streaming data pipelines and contribute to the development of reusable data pipeline components, templates and utilities using established engineering standards, patterns, and reference architectures. * Assist with onboarding new data sources by performing source data profiling, documenting assumptions, and validating data completeness and quality. * Implement data transformations and analytical models to produce curated, analytics‑ready datasets that support reporting and advanced analytics use cases. * Support schema evolution and change management to minimize downstream impact when source data changes. * Collaborate with data analysts, data architects, and product teams to understand data requirements and translate them into well‑defined technical solutions. * Apply data quality checks, validation rules, and monitoring; support the investigation and resolution of data issues and defects. * Support optimization efforts by identifying inefficient queries or unnecessary data processing patterns. * Create and maintain clear documentation for pipelines, data models, and business logic to support transparency, reuse, and operational support. * Participate in code reviews, testing, and CI/CD processes to ensure engineering quality and consistency. * Support production operations, including incident triage, root cause analysis, and corrective actions, in partnership with Data Ops. * Assist in maintaining dashboards or alerts that surface data reliability issues before they impact consumers. * Adhere to governance‑by‑design principles, implementation of data security and privacy controls, including role‑based access, encryption standards, and data classification. * Support the implementation of metadata management practices, including dataset descriptions, data lineage, and ownership information. * Execute unit and integration tests for data pipelines to validate transformations, business rules, and expected outputs. * Participate in sprint planning and backlog refinement, providing input on effort, dependencies, and technical considerations. Skills and Experience: Skills \& Capabilities * English level B2\+ * Proficiency in SQL and at least one programming language, such as Python. * Working knowledge of cloud‑native data services and concepts such as storage layers, compute separation, and cost‑aware design. * Experience integrating from diverse sources including APIs, CSV, JSON, XML, Dataverse and different databases into centralized data platforms. * Solid understanding of ETL / ELT concepts, data modeling techniques (dimensional and analytical models), and the data lifecycle. * Familiarity with modern data platforms (Snowflake or MS Fabric), including data warehouse, data lake, or lakehouse architectures. * Exposure to semantic layers or analytics consumption patterns (e.g., BI tools, metrics definitions). * Experience with version control and foundational CI/CD practices. * Strong analytical thinking, problem‑solving, and collaboration skills. * Ability to learn quickly and contribute effectively within a team‑oriented, Agile delivery environment. * Awareness of data privacy regulations and secure data handling practices in enterprise environments. * Strong written communication skills for documenting technical decisions and explaining data concepts to non‑technical stakeholders. Education / Professional Experience/ Qualifications * Bachelor's degree in Computer Science, Engineering, Information Systems, Analytics, or equivalent practical experience. * 1–3 years of relevant experience in data engineering, analytics engineering, or related technical roles.