Primary Duties:
- Identify and develop scalable and performant solutions.
- Work across discipline to shape product strategy and execution.
- Develop the foundations of code architecture and quality.
- Mentor and coach engineers.
- Set and uphold the standard for engineering processes to support high-quality engineering.
Minimum Qualifications:
- BS/BTech (or higher) in Computer Science, Engineering or a related field required.
- 8+ years of production-level experience as an engineer building highly scalable systems.
- 4+ years of experience acting as a trusted technical decision-maker in a team setting, solving for short-term and long-term business value.
- 4+ years of experience working with SQL or other database querying languages on large multi-table data sets.
- Experience architecting, developing, and deploying large-scale distributed systems at scale.
- Experience with cloud technologies, e.g., AWS, Azure, GCP.
- Experience building continuous integration and continuous development (CI/CD) pipelines.
- Strong familiarity with server-side web technologies (eg: Java, Python, Scala, C#, C++, Go).
Preferred KSAs:
- Deep understanding of one or more tools like Apache Spark, SQL, and Python for data analysis, manipulation, and processing.
- Familiarity with data technologies and architectures (e.g., event based architecture, distributed computing, in-memory data processing).
- Experience working with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, Cassandra, MongoDB), focusing on high-performance querying and optimization for analytical workloads.
- Experience in designing, deploying, and managing data warehouses (eg. Snowflake, Amazon Redshift, etc.) for analytics and business intelligence applications.
- Knowledge of data partitioning, sharding, and indexing strategies to ensure optimal performance in high-load environments.
- Proficiency in designing data models that support analytical requirements, ensuring efficient data retrieval and storage.
- Knowledge of data pipeline architecture, including ETL/ELT processes, batch, and real-time data processing.
- Skilled in optimizing data pipelines for scalability and performance, ensuring efficient data ingestion, storage, and retrieval.
- Ability to use caching strategies and indexing techniques to reduce query and processing times.
- Knowledge of tools for data pipeline creation and orchestration such as Apache Airflow, AWS Glue, and Apache Kafka.
- Knowledge of data security principles and ensuring compliance with regulations (e.g., GDPR, HIPAA) through proper data governance practices.
Physical Requirements:
- Sitting for prolonged periods of time. Extensive use of computers and keyboard. Occasional walking and lifting may be required.
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