Uber Recruitment Of Staff Data Engineer | Great Opportunity

As a Staff Data Engineer, you’ll lead records structure, design scalable pipelines, and optimize information workflows. You’ll collaborate move-functionally, making sure sturdy facts infrastructure and using innovation in information solutions, leveraging superior analytics and machine-gaining knowledge for impactful insights.

Job Role: Staff Data Engineer
Salary: ₹10.9 Lakhs per year
Location: Bengaluru, Karnataka
Company: Uber
Qualification: Bachelor’s Degree

About Company:

Staff Data Engineer

Uber Technologies, Inc. Is an international transportation and technology company situated in San Francisco, California. Known for its ride-hailing app, Uber connects passengers with drivers of motors for rent. Beyond transportation, Uber provides meal transport (Uber Eats) and freight shipping offerings. Founded in 2009, Uber operates in over 900 metropolitan regions worldwide, remodeling urban mobility and logistics.

Role Overview:

Uber is seeking a Staff Data Engineer in Bengaluru to steer critical projects in data excellence and reliability throughout its Mobility company. This role calls for 10+ years of enjoyment in Big Data technologies like Spark, Hive, and Kafka, with expertise in information modeling and programming languages such as Java, Scala, and Python. The best candidate will force unified facts models, mentor engineers, and innovate solutions for superb record flows crucial for commercial enterprise selections.

Roles and Responsibilities Of Staff Data Engineer:

The position of a Staff Data Engineer typically includes leading and handling information engineering tasks within an organization. Here are the top duties.

Leadership:

Providing technical leadership and mentoring junior data engineers, guiding them in great practices and making sure of first-class deliverables.

Data Architecture:

Designing and imposing scalable, robust facts pipelines and architectures that aid records ingestion, processing, and storage desires.

Data Modeling:

Developing and optimizing records models and schemas to aid analytical and operational necessities, ensuring facts integrity and overall performance.

Tool Selection and Implementation:

Evaluating, deciding on, and implementing appropriate tools and technologies for statistics storage, processing, and analytics, such as databases, data warehouses, and ETL frameworks.

Collaboration:

Collaborating with cross-useful groups which include facts scientists, analysts, and commercial enterprise stakeholders to recognize facts necessities, offer data infrastructure solutions, and guide statistics-driven decision-making. Staff Data Engineer

Qualification:

Educational Background: A degree in Computer Science with at least 10+ years of industry experience, including 5+ years in the Data domain.

Skills Required For Staff Data Engineer:

For a Staff Data Engineer position, several key competencies are critical to excel within the role:

Advanced-Data Engineering:

Proficiency in designing, implementing, and optimizing complicated statistics pipelines with the use of equipment like Apache Spark, Hadoop, or similar frameworks. Strong information on statistics warehousing standards and ETL approaches is critical.

Programming and Scripting:

Expertise in programming languages together with Python, Java, Scala, or R is necessary for constructing scalable statistics processing structures and appearing records analysis responsibilities. Knowledge of SQL for querying relational databases is likewise useful.

Big Data Technologies:

Hands-on enjoyment with huge records technologies along with Hadoop atmosphere (HDFS, Hive, Pig, and so on.), Spark, Kafka, and NoSQL databases (like MongoDB, and Cassandra) is critical for coping with massive volumes of data efficaciously.

Data Modeling and Architecture:

Ability to lay and put into effect data fashions that guide business necessities. Understanding of information architecture principles, consisting of facts lakes, data warehouses, and records marts.

Cloud Platforms:

Familiarity with cloud systems like AWS, Azure, or Google Cloud for deploying and coping with statistics infrastructure. Experience with cloud-primarily based facts services together with AWS EMR, Azure HDInsight, or Google BigQuery is high quality.