Job Description

Manager - Big Data Engineering - Wallet & Apple Pay - Austin

Austin, Texas, United States

Software and Services

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Summary

Posted: Feb 10, 2021

Role Number:200222514

Looking for hardworking, passionate and results-oriented individuals to join our team to build data foundations and tools to craft the future of Wallet and Apple Pay. You will design and implement scalable, extensible and highly-available data pipelines on large volume data sets, that will enable impactful insights & strategy for payment products. Our culture is about getting things done iteratively and rapidly, with open feedback and debate along the way. We believe analytics is a team sport, but we strive for independent decision-making and taking smart risks. Our team collaborates deeply with partners across product and design, engineering, and business teams: our mission is to drive innovation by providing the business and data scientist partners outstanding systems to make decisions that improve the customer experience of using our services. Reporting to the head of Wallet Payments & Commerce Data Engineering & BI, this person will be responsible for data collection and processing for core lines of business and will collaborate with various data analysts, instrumentation experts and engineering teams to identify requirements that will derive the creation of data pipelines. You will work closely with the application server engineering team to understand the architecture and internal APIs involved in upcoming and ongoing projects related to Apple Pay. We are seeking an outstanding person to play a lead role in helping the analysts & business users make decisions using data and visualizations. You will partner with key stakeholders across the engineering, analytics & business teams as you design and build query-friendly data structures. The ideal candidate is a self-motived team player, skilled in a broad set of Big Data processing techniques with the ability to adapt and learn quickly, provide results with limited direction, and choose the best possible data processing solution is a must.



Key Qualifications

Key Qualifications

* Demonstrated ability with ETL, BI & Data Analytics
* 5+ years of professional experience with Big Data systems, pipelines and data processing
* Deep expertise in Data Architecture, Data Modeling and task estimations
* Practical hands-on experience with technologies like Apache Hadoop, Apache Pig, Apache Hive, Apache Sqoop & Apache Spark
* Ability to understand API Specs, identify relevant API calls, extract data and implement data pipelines & SQL friendly data structures
* Expertise in Python, Unix Shell scripting and Dependency driven job schedulers
* Expertise in Core JAVA, Oracle, Teradata, Snowflake and ANSI SQL
* Knowledge on Scala, Splunk and Data visualization tools such as Tableau is a plus
* Ability to deliver consistent high quality results while working in a dynamic & fast paced environment with extreme attention to detail and skills to self-audit work
* Passion for excellence across all disciplines



Description

Description

Lead, Mentor and Manage a pool of hardworking Big Data Engineers Design and Build scalable data sets based on engineering specifications from the available raw data and derive business metrics/insights Understand and Identify server APIs that needs to be instrumented for data reporting and align the server events for execution in already established data pipelines Discover and understand complex data sets, identify and formulate correlational rules between heterogeneous data sources for effective analytics and reporting Successfully bridge engineering and business with ability to speak the common language of both Work hand in hand with the Platform and DevOps team for platform/tool improvements, supervising and alerting on various data pipelines and jobs



Education & Experience

Education & Experience

Minimum of bachelors degree, preferably in Computer Science, Information Technology or EE, or relevant industry experience is helpful



Additional Requirements

Additional Requirements

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