Data Engineering Manager – Legal
| Company | Meta |
|---|---|
| Location | Seattle, WA, USA, Washington, DC, USA, San Francisco, CA, USA, Austin, TX, USA, Los Angeles, CA, USA, Menlo Park, CA, USA, New York, NY, USA |
| Salary | $173000 – $242000 |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Expert or higher |
Requirements
- Bachelor’s degree in Engineering, Computer Science, Math or a related quantitative field.
- 10+ years of experience designing and building data engineering solutions that solve complex, ambiguous problems or scale horizontally to provide value across many teams and consumers.
- 3+ years of people management experience. Experience hiring and managing highly skilled data engineers, providing mentorship, guidance, and career development to the members of the team.
- Experience with SQL, Python development, data modeling, custom ETL (Extract, Transform, Load) design, implementation and maintenance.
- Experience working with cloud or on-prem Big Data/Massively Parallel Processing analytics platform (i.e. Netezza, Teradata, AWS Redshift, Google BigQuery, Azure Data Warehouse, or similar).
- Experience with workflow management engines (i.e. Airflow, Luigi, Prefect, Dagster, Google Cloud Composer, AWS Step Functions, Azure Data Factory, Control-M).
- Experience owning and delivering business initiatives from idea to implementation.
Responsibilities
- Partner with leadership, engineers, program managers and data analysts to understand data needs.
- Design, build and launch efficient and reliable data pipelines transforming data into useful report ready datasets.
- Broad range of partners equates to a broad range of projects and deliverables including data pipeline frameworks, datasets, measurements, services, tools and process.
- Use your data and analytics experience to identifying and addressing data gaps, proactively monitor and detect data quality issues and partner to establish a self-serve environment.
- Leverage data and business principles to automate data flow, detect business exceptions, build diagnostics, improve both business and data knowledge base.
- Build data expertise and own data quality for your areas.
Preferred Qualifications
- Experience with more than one coding language.
- Experience in designing and implementing real-time pipelines.
- Experience with data quality and validation.
- Experience with SQL performance tuning and e2e process optimization.
- Experience with anomaly/outlier detection.
- Experience with notebook-based Data Science workflow.
- Experience querying massive datasets using Spark, Presto, Hive, Impala, etc.
- Proven track record of driving large cross-functional standards and best practices and setting the bar when it comes to design and build quality.