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

Santa Clara Valley (Cupertino), California, United States
Software and Services

Summary

Posted:
Role Number:200184368
Looking for hardworking and results-oriented individuals to join our team to build data foundations 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 and tools to make decisions that improve the customer experience of using our services. This will include using large and complex data sources, helping derive measurable insights, delivering multifaceted and intuitive decision tools, and bringing our data to life via amazing visualizations. Working with 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 specialists 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 partners across the engineering, analytics & business teams as you design and build query data structures.

Key Qualifications

  • 10+ yrs exp with ETL, BI & Data Analytics
  • 5+ years of professional experience with Big Data systems, pipelines and data processing
  • 3+ years experience with mentoring and leading data engineers
  • 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
  • Understanding on various distributed file formats such as Apache AVRO, Apache Parquet and common methods in data transformation
  • Expertise in Python, Unix Shell scripting and Dependency driven job schedulers
  • Expertise in Core JAVA, Oracle, Teradata, Snowflake and ANSI SQL and familiarity with Apache Oozie and PySpark
  • 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 multifaceted & fast environment with extreme attention to detail and ability to self-audit work

Description

- Lead, Mentor and Manage a pool of hardworking data engineers - Translate business requirements by business team into data and engineering specifications - Design and Build scalable data sets based on engineering specifications from the available raw data and derive business metrics/insights - Closely collaborate with business partners to define and implement the data engagement relationships required with partners - Understand and Identify server APIs that needs to be instrumented for data analytics 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 - Work hand in hand with the Platform and DevOps team for platform/tool improvements, monitoring and alerting on various data pipelines and jobs

Education & Experience

Minimum of bachelor’s degree, preferably in Computer Science, Information Technology or EE, or relevant industry experience is preferred

Additional Requirements