- Job Role: Analytics Data Engineer
- Salary: $113,400–$215,300 a year
- Location: Vancouver, BC
- Company: Apple
- Qualifications: bachelor’s degree
- Experience: 2-4 years
ABOUT APPLE
Apple Canada is the Canadian division of Apple Inc., a global chief in client electronics, software, and virtual offerings. Known for its innovative products which include the iPhone, Mac computer systems, iPad, Apple Watch, and AirPods, Apple has built popularity in an exquisite, character-satisfactory era. In Canada, Apple operates retail shops in number-one towns, offering customers personalized guides, upkeep, and product consultations. The enterprise additionally gives a collection of services that incorporate Apple Music, iCloud, and the App Store.
Apple’s determination to sustainability is meditated in its efforts to reduce its environmental footprint, with a focus on renewable power and recycling. As a part of its venture, Apple strives to foster creativity, schooling, and economic growth within Canada through its products, services, and community engagement projects. The organization continues to pressure technological advancement and innovation within the Canadian marketplace.
Job Overview:
An Analytics Data Engineer is chargeable for designing, building, and retaining statistics infrastructure to help records analysis and enterprise intelligence. This feature entails operating with huge datasets, imposing information pipelines, making sure information is first-class, and optimizing information garage answers. The engineer collaborates carefully with facts scientists, analysts, and exceptional teams to allow powerful data-pushed preference-making. Key abilities include skillability in SQL, ETL techniques, cloud structures (AWS, Azure), and programming languages like Python or Java. The function requires a sturdy know-how of records structure, facts modeling, and standard overall performance optimization strategies.
Requirements and Skills for an Analytics Data Engineer:
- Educational Background:
- A bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related area is generally required. Advanced tiers (Master’s, PhD) may be preferred for added senior roles but aren’t usually obligatory.
- Technical Skills:
- SQL & Data Querying: Proficiency in SQL is a ought to for growing complicated queries, optimizing general performance, and coping with relational databases.
- ETL (Extract, Transform, Load): Experience designing, constructing, and keeping ETL pipelines to device and flow data from multiple assets into storage structures.
- Programming Languages: Knowledge of programming languages collectively with Python, Java, or Scala is important for writing custom scripts, automating responsibilities, and manipulating statistics.
- Data Warehousing & Database Management: Experience with records warehouses (e.g., Amazon Redshift, Google BigQuery, Snowflake) and relational databases (e.g., MySQL, PostgreSQL) is critical for managing massive datasets efficaciously.
- Big Data Technologies: Familiarity with massive information frameworks which include Hadoop, Spark, or Kafka, which might be often used for handling large-scale statistics processing.
- Cloud Platforms: Proficiency in cloud services (AWS, Azure, Google Cloud) for managing records storage, compute belongings, and infrastructure is becoming more and more important.
- Data Modeling: Understanding how to shape and put together data effectively for analytical capabilities, which include ideas like star schemas, snowflake schemas, and normalization.
- Version Control Systems: Experience with equipment like Git for coping with code and collaborating inside development teams.
- Data Quality & Monitoring: Knowledge of statistics amazing standards, blunders dealing with, and monitoring gadgets too ensure data integrity and pipeline normal overall performance.
- Analytical Skills:
- Strong functionality to investigate and interpret huge datasets, find out tendencies, and make data-pushed guidelines.
- Ability to debug, troubleshoot, and optimize complicated records pipelines and workflows.
- Soft Skills:
- Collaboration: Ability to play in cross-purposeful organizations along with records scientists, organization analysts, and different stakeholders.
- Communication: Strong written and verbal communication talents to document technical techniques, and produce insights and suggestions in reality to non-technical stakeholders.
- Problem-solving troubleshooting competencies and the functionality to suppose significantly and treat complex issues associated with statistics structures and workflows.
5. Experience:
- Typically, 2-five years of revel in facts engineering, analytics, or any associated issues is preferred.
- Previous experience in coping with statistics pipelines and optimizing them for speed rate efficiency is a super benefit.
- Preferred Skills:
- Familiarity with device learning principles for destiny integration with facts technological know-how teams.
- Knowledge of containerization technology like Docker or Kubernetes for deployment and orchestration of information infrastructure.
Click Here to Apply Now
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