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.
