Senior Data Engineer
- Compensation
- Not disclosed
- Location
- Pasadena, US
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Required: 7+ years of development experience in data engineering within a production environment (internships and academic settings excluded).
- Proven experience working with large datasets exceeding 100M+ records or multiple terabytes.
- Required: 5+ years of development experience in highly scalable, distributed systems and cluster architectures using AWS and utilizing EMR.
- Required: 5+ years of hands-on programming experience with Python.
- Required: 5+ years of professional experience working in big data ecosystems; Spark is required; PySpark is preferable.
- Required: 5+ years of experience with SQL, schema design, and dimensional data modeling.
- Required: 5+ years of professional experience working with dataflow orchestration tools, such as Airflow.
- Required: 2+ years of experience with non-relational databases (e.g., DynamoDB, Elasticsearch, etc.).
- Required: A bachelor’s degree in Computer Science, Information Systems, Mathematics, or a related field is required.
Full description
About Spokeo:
Join our mission to make the world more transparent with data.
Spokeo is a people intelligence platform that helps over 18 million monthly visitors reconnect with friends, reunite with families, and build trust in new relationships. Thousands of companies also trust Spokeo’s 60 billion public records to improve customer research, help verify information, and prevent fraud.
Founded in 2006, Spokeo has built a dedicated, remote-first team with an average tenure of 6.9 years. It has earned recognition from Comparably as a Best Company for Compensation, Employee Happiness, Perks and Benefits, Support for Women, Work-Life Balance, and CEO Leadership.
About this Opportunity
As a Senior Data Engineer at Spokeo, you will develop, optimize, and improve our data systems, including ETL pipelines, storage, and entity resolution. This involves working with infrastructure built in AWS, including Airflow, PySpark, EMR, S3, DynamoDB, and more. This role will help build and improve data products, automation platform features, analytical software packages, and data pipeline orchestration tools.
What You’ll Do:
- Build infrastructure and data automation pipelines to ingest, process, and load data from various sources. Automate and integrate new components into the data pipeline.
- Collaborate with stakeholders and data science teams to develop data products, including entity resolution and best selection, to efficiently execute product vision and strategy in alignment with organizational goals and priorities.
- Create unit and stress-test components to monitor technical performance and ensure that identified issues are resolved.
- Develop data analysis tools to provide data insights and capture key metrics.
- Research solutions and maintain technical documentation.
- Follow best practices for data governance, quality, cleansing, and other ETL-related activities.
Who You Are:
- Required: 7+ years of development experience in data engineering within a production environment (internships and academic settings excluded).
- Proven experience working with large datasets exceeding 100M+ records or multiple terabytes.
- Required: 5+ years of development experience in highly scalable, distributed systems and cluster architectures using AWS and utilizing EMR.
- Required: 5+ years of hands-on programming experience with Python.
- Required: 5+ years of professional experience working in big data ecosystems; Spark is required; PySpark is preferable.
- Required: 5+ years of experience with SQL, schema design, and dimensional data modeling.