Data Engineer, Analytics (Family Ecosystems)

Our more experienced data engineers are clearly characterized by in-depth technical experience, subject matter expertise and proven progression in leadership responsibility. If you have an interest in owning important and critical problem areas and influencing by building robust company-wide data foundation and tooling, this is the right role for you. You will get to impact the End-to-End (E2E) suite of big-data tools and products that play a critical part in the day-to-day development lifecycle of Data Engineers, Data Scientists, ML Engineers, Research Scientists & Software Engineers.

In this role, you will work closely with Data Infrastructure/Product Software Engineering and Product Management teams to foundationally evolve long-term, architecture-driven, E2E analytics development cycle and the Data Products, Platforms, Tools and Infrastructure stacks that underlie such as – Logging, Streaming, Batch/Compute engines (Presto, Spark), Language/APIs, Semantic Data and Metadata models, ML workflows/models, Consumption workflows (Visualization/Notebooks), Data Discovery and so on.

You will define and find solutions to complex and often ambiguous problems as a Subject Matter Expert. You will be leveraging your deep knowledge and experience to collaboratively define technical vision, strategy and architecture in three key areas – Semantic Data and Metadata modeling, Large-scale analytics architecture (covering Logging, ETL and Consumption stacks) and Big Data development lifecycle (coding, testing, deploying, discovery etc.).

A few examples of the impact and influence of your work: Consistent E2E Data Model and Definition-driven metrics such as Message Sends across the Family of Apps, Data model and metadata-driven, foundational, company-wide Analyics APIs such as User Retention, Evolving Dataframe APIs, Data Models and company-wide lifecycle development from Logging through Consumption through critical company-wide analytics use cases and Enabling consumption and adhoc exploratory workflows for Data Scientists by helping envision and implement large-scale analytics architecture use cases.