Senior Data Engineer

Spokeo•Pasadena, CA

16h•Remote

About The Position

As a Senior Data Engineer at Spokeo, you will develop, optimize, and improve our data systems such as ETL data, pipeline, 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.

Requirements

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.
2+ years of development experience in highly scalable, distributed systems and cluster architectures using AWS.
5+ years of hands-on programming experience with Python.
5+ years of professional experience working in big data ecosystems, Spark is required; PySpark is preferable.
3+ years of experience with SQL, schema design, and dimensional data modeling.
2+ years of professional experience working with dataflow orchestration tools, such as Airflow.
2+ years of experience with non-relational databases (e.g., DynamoDB, Elasticsearch, etc.).
A bachelor’s degree in Computer Science, Information Systems, Mathematics, or a related field is required.

Responsibilities

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.