Lead Data Engineer, Analytics Platform

Charleston (WV)

On-site

USD 210,000 - 281,000

Full time

14 days+

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Job summary

Meta is seeking a technical leader for its Data Engineering team to partner with Product Managers, Data Scientists and Software Engineers in building a scalable data platform for the future. You will own data architecture for large-scale projects, govern data security and privacy, and drive end-to-end analytics across Meta products.

You will mentor teammates, define SLAs for datasets, and optimize pipelines and dashboards to enable faster delivery of data artifacts.

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
10+ years experience in the data warehouse space.
10+ years experience in custom ETL design, implementation and maintenance.
10+ years experience with object-oriented programming languages.
10+ years experience with schema design and dimensional data modeling.
10+ years experience in authoring SQL statements.
Experience analyzing data to identify deliverables, gaps and inconsistencies.
Experience managing and communicating data warehouse plans to internal clients.

Responsibilities

Proactively drive the vision for data foundation and analytics for cross-platform components across Meta products.
Own data architecture for large-scale projects, evaluating design and operational cost-benefit tradeoffs.
Create and contribute to frameworks that improve data logging and triage issues.
Build cross-functional relationships with Data Scientists, Product Managers and Software Engineers.
Define and manage SLA for all data sets in ownership areas.
Determine and implement security model based on privacy requirements and governance processes.
Design, build, and launch sophisticated data models and visualizations for various products.
Solve data integration challenges using ETL patterns and diverse data sources.
Assist in owning production processes and optimizing complex code.
Optimize pipelines, dashboards, frameworks, and systems for easier data artifact development.
Influence product and cross-functional teams to identify data opportunities.
Mentor team members with actionable feedback.