Job Role: Data Engineer
Salary : Rs 7,50,000–₹8,10,000 per year
Location: Gurugram, Delhi
Company: FedEx
Qualification: B.Tech/B.E.
Experience: 5 – 10 years
ABOUT FEDEX
FedEx Corporation, based in 1971 through Frederick W. Smith, is an international leader in logistics and transport services. Headquartered in Memphis, Tennessee, the business enterprise operates via several segments, collectively with FedEx Express, FedEx Ground, FedEx Freight, and FedEx Services. It gives a massive variety of shipping solutions, from single-day courier offerings to freight transportation, serving thousands and thousands of customers worldwide.
FedEx is an idea for its innovation in monitoring systems and shipping management, emphasizing velocity, reliability, and performance. The corporation has a sturdy cognizance of sustainability, aiming to lessen its carbon footprint and decorate operational overall performance. With a huge network of facilities and automobiles, FedEx performs a crucial feature in global commerce, connecting organizations and individuals across the globe. Its commitment to patron satisfaction and technological development keeps pressure on its fulfillment and increases within the aggressive logistics enterprise.
Job Description
A Data Engineer designs develops and continues statistics pipelines and architectures to assist records-driven choice-making. The artwork with massive datasets, making sure records are integrity and quality, and putting into effect facts garage answers using generation like SQL, NoSQL, and cloud systems. Responsibilities encompass integrating statistics from numerous assets, optimizing question overall performance, and taking part with statistics scientists and analysts to understand and meet their records needs. Strong capabilities in programming, database management, and facts warehousing are vital, together with trouble-solving capabilities and attention to element.
Responsibilities For a Data Engineer:
- Design and Build Data Pipelines: Develop and maintain sturdy, scalable facts pipelines to guide records integration, processing, and storage from numerous belongings, making sure information is available for evaluation and reporting.
- Data Architecture and Storage: Design and placed into effect information architectures and garage solutions, making use of era which include SQL databases, NoSQL databases, and cloud-primarily based data services (e.g., AWS Redshift, Google BigQuery, Azure SQL).
- Data Integration: Integrate statistics from various assets, such as APIs, outdoor databases, and inner structures, making sure seamless data goes with the flow and consistency at some stage in systems.
- Performance Optimization: Optimize statistics garage and question overall performance with the resource of tuning database configurations, indexing, and partitioning strategies to ensure inexperienced statistics retrieval and processing.
- Data Quality and Integrity: Monitor and implement records first-rate and integrity necessities, imposing validation checks and errors dealing with methods to preserve accurate and reliable datasets.
- Collaboration: Work intently with records scientists, analysts, and exceptional stakeholders to recognize records necessities, imparting technical help and making sure facts are available for assessment and reporting wishes. (Data Engineer)
- Documentation: Create and maintain comprehensive documentation for facts pipelines, architectures, and techniques, making sure clarity and continuity for future development and troubleshooting.
- Troubleshooting and Support: Diagnose and solve records-related troubles, which include overall performance bottlenecks, records inconsistencies, and integration disasters, offering well-timed answers to reduce disruptions.
- Data Security: Implemented and placed into impact information protection functions to protect sensitive records, ensuring compliance with information safety rules and organizational guidelines.
- Continuous Improvement: Stay present day-to-day with emerging information technology and high-quality practices, recommending and enforcing enhancements to decorate statistics engineering strategies and abilities.
Qualifications For a Data Engineer:
- Education: Bachelor’s diploma in Computer Science, Engineering, Data Science, or a related location. Advanced ranges or certifications in statistics engineering are a plus.
- Experience: Proven revel in (normally 3 years) as a Data Engineer or in a similar feature, with a sturdy portfolio of information engineering duties and accomplishments.
- Technical Skills: Proficiency in programming languages such as Python, Java, or Scala. Strong revel in SQL and NoSQL databases (e.g., MySQL, MongoDB). Familiarity with statistics warehousing answers and ETL equipment.
- Cloud Platforms: Experience with cloud-based total facts offerings and systems, which include AWS, Google Cloud Platform, or Azure, which incorporates offerings like Redshift, BigQuery, and Azure Data Factory.
- Problem-Solving: Excellent analytical and problem-solving abilities, with the capacity to troubleshoot complicated records problems and place them into impact powerful solutions. (Data Engineer)
- Communication: Strong verbal and written conversation abilities, with the capacity to genuinely give an explanation for technical thoughts and collaborate successfully with go-sensible businesses.
- Attention to Detail: High degree of accuracy and hobby to element, making sure statistics are satisfactory and consistent in all engineering tactics.
- Adaptability: Ability to work in fast-paced surroundings and adapt to converting generation and challenge requirements.
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