How would Facebook scale to the next billion users? The Infrastructure Strategy group is responsible for the strategic analysis to support and enable the continued growth critical to Facebook’s infrastructure organization.

We are looking for a Data Engineer to not only build data pipelines but also extend the next generation of our data tools. As a Data Engineer, you will develop a clear sense of connection with our organization and leadership - as Data Engineering is the eyes through which they see the product.

As a member of Infrastructure Strategy Data Engineering, you will belong to a centralized Data Science/Data Engineering team who partners closely with teams in Facebook’s Infrastructure organization. Through the consulting-nature of our team, you will contribute to a variety of projects and technologies. Projects include analytics, ML modeling, tooling, services, and more.

This position has a 16-week duration beginning in early 2021. All candidates should be able to commit to a consecutive 16-week timeframe for successful completion of the program. At the end of the term, those who have demonstrated their abilities to succeed and make an impact at Facebook will be considered for a full-time position. Candidates are encouraged to apply early!

This program is open to all candidates that have been out of the workforce for two or more years immediately prior to the start of the program.

Return to Work - Infrastructure Data Engineer Responsibilities

• Partner with leadership, engineers, program managers and data scientists to understand data needs.

• Design, build and launch extremely efficient and reliable data pipelines to move data across a number of platforms including Data Warehouse, online caches and real-time systems.

• Communicate, at scale, through multiple mediums: Presentations, dashboards, company-wide datasets, bots and more.

• Educate: Use your data and analytics experience to ‘see what’s missing’, identifying and addressing gaps in existing logging and processes.

• Broad range of partners equates to a broad range of projects and deliverables: ML Models, datasets, measurement, processes, services, and tools.

• Leverage data and business principles to solve large scale web, mobile and data infrastructure problems.

• Build data expertise and own data quality for your areas.

Minimum Qualifications

• 4+ years of related experience, all occurring after graduation.

• 4+ years of coding/development experience.

• 4+ years of SQL experience.

• 3+ years experience with Data Modeling.

• 2+ years of experience with workflow management engines (i.e. Airflow, Luigi, Prefect, Dagster, digdag.io, Google Cloud Composer, AWS Step Functions, Azure Data Factory, UC4, Control-M).

• Experience analyzing data to discover opportunities and address gaps.

• 4+ years experience in custom ETL design, implementation and maintenance.

• Experience working with cloud or on-prem Big Data/MPP analytics platform (i.e. Netezza, Teradata, AWS Redshift, Google BigQuery, Azure Data Warehouse, or similar).

Preferred Qualifications

• Experience with more than one coding language.

• Experience designing and implementing real-time pipelines.

• Experience with data quality and validation.

• Experience with Airflow.

• Experience with SQL performance tuning and E2E process optimization.

• Experience with database and query performance tuning.

• Experience querying massive datasets using Spark, Presto, Hive, Impala, etc.

• Experience with notebook-based Data Science workflow.

• MS in CE/EE/CSE or computational sciences.