Data Engineer, Analytics (Family Ecosystems)

nnOur 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.nn nnData Engineer, Analytics (Family Ecosystems) Responsibilitiesnn nnCraft and own the optimal data processing architecture and systems for new data and ETL pipelines/analytics applicationsnn nnBuild and data (dimensional) model core datasets and analytics applications and make them scalable and fault-tolerantnn nnDrive comprehensive Technical Vision on fundamental aspects and evolution of Analytics/Data Infra Foundation/Toolingnn nnDefine and disseminate technical or product strategy clearly for effective outcomesnn nnArticulate strategy within teams, effectively communicate with cross-functionalnn nnarticulate solutions and influence leadershipnn nnCollaborate and work with different cross-functional partners
– Data Infrastructure, Product Software Engineering, Data Engineering and Product Management teams
– on use cases sto foundationally evolve long-term, architecture-driven, E2E analytics development cyclenn nnTechnically influence within the function and cross-functional communitynn nnBuild visualizations to provide insights into the data & metricsnn nnImmerse yourself in all aspects of the product, understand the problems, and tie them back to data engineering solutionsnn nnDrive internal process improvements and automating manual processesnn nnProvide ongoing proactive communication and collaboration throughout the organizationnn nnMinimum Qualificationsnn nn4+ years’ experience in the data warehouse spacenn nn4+ years’ experience working with either a MapReduce or an MPP systemnn nn7+ years’ experience in writing complex SQL, Dataframe APIs and ETL processesnn nn4+ years’ experience with object-oriented programming languagesnn nn7+ years’ experience with schema design and dimensional data modelingnn nnPreferred Qualificationsnn nnBS/BA in Technical Field, Computer Science or Mathematicsnn nnKnowledge in Python or Java or Scala or Pandasnn nnExperience analyzing data to identify deliverables, gaps, and inconsistenciesnn nnExperience mentoring team members in their careersnn nnExperience collaborating, defining and communicating complex technical concepts to a broad variety of audiences ariety of audiencesnn nnExperience scaling analytics architecture and worked with open source big-data stacks (Spark, Koalas etc.)nn