FPT Software ★ 3.8

Databricks Platform Engineering
• Create and manage workflows that integrate various retail data sources into a unified data architecture.
• Design, implement, and optimize scalable data pipelines on Databricks to process and analyze high- volume retail data.
• Utilize Delta Lake, Apache Spark, SQL, and Databricks’ features to ensure efficient data transformation, processing, and storage.
Retailing Domain Expertise Application
• Apply deep knowledge of the retail industry to develop solutions that optimize key business processes like demand forecasting, demand allocation, pricing optimization, inventory management, and customer behavior analysis.
• Leverage historical sales data, customer interaction data, and product catalog data to develop actionable insights for retail operations.
• Collaborate with stakeholders to ensure data pipelines are aligned with retail business goals and help drive data- driven decision- making.
Data Integration & Processing
• Data Modeling for retailing : Data warehouse
• Ensure seamless data flow and timely delivery of information for business intelligence (BI) tools and machine learning models.
• Integrate and harmonize data from diverse sources such as transactional systems, inventory management tools, customer behavior platforms, and third- party retail data APIs.
Data Quality & Governance
• Implement data governance practices, ensuring compliance with industry regulations and secure handling of sensitive retail data.
• Apply data lineage and tracking to maintain transparency and traceability of data across retail data pipelines.
• Define and enforce data quality standards specific to retail data, ensuring data accuracy and integrity in product data, sales trends, and customer metrics.
Collaboration & Continuous Improvement
• Document and share best practices in data engineering, specifically in the context of retail analytics
• Work closely with retail analysts, data scientists, and business teams to understand retail- specific challenges and propose effective data solutions.
• Continually evaluate and optimize data processing pipelines, ensuring alignment with evolving retail needs and technology advancements.

• Education: Bachelor&039;s degree in Computer Science, Engineering, Data Science, or a related field. A Master’s degree or relevant certifications are a plus.

Experience
• Knowledge of retail business concepts such as demand forecasting, customer segmentation, pricing optimization, inventory management, and omni- channel analytics.
• Strong experience with cloud platforms (AWS, Azure, or GCP) and their associated data tools for processing and storing retail data.
• Hands- on experience with SQL, Python, and Scala for building and optimizing data pipelines.
• Minimum of 3- 5 years of experience in data engineering, with a focus on the retail domain (e.g., working with retail data such as sales transactions, customer data, inventory systems).
• Proven experience with Databricks and its ecosystem (Delta Lake, Apache Spark, Databricks notebooks) for building scalable data pipelines.

Technical Skills
• Knowledge of data warehousing tools and technologies like Snowflake, BigQuery, or Redshift is a plus.
• Familiarity with retail- specific analytical models, such as sales trend analysis, predictive inventory models, and price elasticity models.
• Solid understanding of data storage technologies such as S3, Azure Data Lake Storage (ADLS), or Google Cloud Storage for large- scale retail data.
• Expertise in building and optimizing ETL workflows using Databricks and Spark for big data processing.

Soft Skills
• Strong communication skills, with the ability to explain complex data concepts to retail business stakeholders.
• Ability to work independently, prioritize tasks, and meet deadlines in a fast- paced retail environment.
• Excellent problem- solving and troubleshooting skills, particularly in the context of retail data complexities.

Preferred Qualifications
• Experience integrating machine learning models into retail data pipelines for predictive analysis.
• Previous exposure to business intelligence (BI) tools such as Tableau, Power BI, or Looker, and how they relate to retail- specific analytics.
• Familiarity with CI/CD practices for data pipeline deployment, including version control using tools like Git.

• International exposure and career growth across global locations.
• Global and inclusive workplace with monthly cross- cultural events.
• Attractive annual summer vacation allowance.
• Work- life balance benefits with a flexible leave policy and annual health check- ups to support employee well- being.
• Sponsored training courses for personal growth and up to 100% coverage for certification costs.
• FPT Care insurance plan tailored for FPT employees.

Cập nhật gần nhất lúc: 2026-02-21 12:30:03