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Sr Software Engineer - Data Engineering

Uber

Sr Software Engineer - Data Engineering

Uber

Bangalore, India

·

On-site

·

Full-time

·

4d ago

About the Role

As an Engineer on the Data Intelligence team, you will be dealing with large scale data pipelines and data sets that are critical and foundational for Uber to make decisions for better customer experience. You will be working on a petabyte scale of analytics data from the multiple Uber applications. Help us build the software systems and data models that will enable data scientists to understand our user behavior better and thrive on the data driven mindset at Uber.

What the Candidate Will Need / Bonus Points

---- What the Candidate Will Do ----

  1. Responsible for defining the Source of Truth (SOT), Dataset designfor multiple Uber teams.
  2. Identify unified data models collaborating with Data Science teams
  3. Streamline data processing of the original event sources and consolidate them in source of truth event logs
  4. Build and maintain real-time/batch data pipelines that can consolidate and clean up usage analytics
  5. Build systems that monitor data losses from the different sources and improve the data quality
  6. Own the data quality and reliability of the Tier-1 & Tier-2 datasets including maitaining their SLAs, TTL andconsumption
  7. Devise strategies to consolidate and compensate the data losses by correlating different sources
  8. Solve challenging data problems with cutting edge design and algorithms.

---- Basic Qualifications ----

  1. 7+ years of extensive Data engineering experience working with large data volumes and different sources of data.
  2. Strong data modeling skills, domain knowledge and domain mapping experience.
  3. Strong experience of using SQL language and writing complex queries.
  4. Experience with using other programming languages like Java, Scala, Python
  5. Good problem solving and analytical skills
  6. Good communication, mentoring and collaboration skills.

---- Preferred Qualifications ----

  1. Extensive experience in data engineering and working with Big data
  2. Experience with ETL or Streaming data and one or more of, Kafka, HDFS, Apache Spark , Apache Flink , Hadoop
  3. Experience backend services and familiarity with one of the cloud platform ( AWS/ Azure / Google cloud)

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuelds progress. What moves us, moves the world - let's move it forward, together.

Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.

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About Uber

Uber

Uber develops, markets, and operates a ride-sharing mobile application that allows consumers to submit a trip request.

10,001+

Employees

San Francisco

Headquarters

$120B

Valuation

Reviews

3.1

10 reviews

Work Life Balance

4.2

Compensation

2.3

Culture

3.5

Career

2.0

Management

2.5

45%

Recommend to a Friend

Pros

Flexible hours and schedule

Meeting different people and cultures

Make your own hours

Cons

Inconsistent and low pay

Safety concerns with passengers

Traffic and difficult drivers

Salary Ranges

23,534 data points

Mid/L4

Mid/L4 · Data Analyst

3 reports

$209,300

total / year

Base

$161,000

Stock

-

Bonus

-

$203,580

$209,300

Interview Experience

5 interviews

Difficulty

3.0

/ 5

Duration

14-28 weeks

Offer Rate

40%

Experience

Positive 80%

Neutral 20%

Negative 0%

Interview Process

1

Application Review

2

Online Assessment

3

Recruiter Screen

4

Technical Phone Screen

5

Case Study/Analytics Test

6

Final Loop/Panel Interview

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Case Study

Technical Knowledge