Skip to content
Software Engineer – Backend
| Company | Tatari |
|---|
| Location | San Francisco, CA, USA |
|---|
| Salary | $140000 – $180000 |
|---|
| Type | Full-Time |
|---|
| Degrees | |
|---|
| Experience Level | Senior |
|---|
Requirements
- 5+ years of experience working in data architecture, data modeling, and building data pipelines & distributed systems at scale.
- Recent accomplishments working with relational and NoSQL data stores, methods, and approaches (STAR, Dimensional Modeling).
- 2+ years of experience with a modern data stack (Kafka, Spark, Airflow, lakehouse architectures, real-time databases, dbt, etc.) and cloud data warehouses such as RedShift, Snowflake.
- Familiarity with cloud computing platforms like Amazon Web Services (AWS) and proficiency in leveraging cloud-based services for data storage, processing, and analytics, such as Amazon S3, EC2, and Lambda.
- Proficiency in programming languages and frameworks commonly used in data engineering, such as FastAPI, Python, Java, Scala, or SQL. Experience with data processing frameworks and tools like Apache Spark (including Databricks), and Hadoop and knowledge of database technologies like SQL databases (e.g., MySQL, PostgreSQL).
- Ability to identify and troubleshoot data-related issues, optimize systems, and propose innovative solutions.
- Excellent communication skills to effectively collaborate with cross-functional teams, stakeholders, and business users and ability to explain technical concepts to non-technical audiences and translate business requirements into technical solutions.
- Experience in providing technical guidance, mentoring junior data engineers, and leading data engineering initiatives and ability to drive projects, prioritize tasks, and manage timelines.
Responsibilities
- Building, managing and optimizing data infrastructure, designing and developing data pipelines, and ensuring the reliability and scalability of data systems.
- Implementing scalable, efficient, and reliable data infrastructure, including data storage, processing, and retrieval systems.
- Developing and maintaining robust and efficient data pipelines to ingest, transform, and deliver data from various sources to data storage and analytical systems.
- Designing and implementing data models and database schemas that support efficient data storage, retrieval, and analysis.
- Building and maintaining ETL processes to extract data from different sources, transform it into a suitable format, and load it into data storage systems.
- Identifying and resolving performance bottlenecks in data pipelines and database systems. Tuning and optimizing queries, indexes, and data storage configurations to improve overall system performance.
- Experience maintaining micro services and APIs responsible for powering customer facing applications.
- Collaborating with cross-functional teams, including data scientists, analysts, and software engineers, to understand their data requirements and provide them with the necessary infrastructure and tools. Mentoring and providing technical guidance to junior data engineers.
- Implementing monitoring systems and practices to ensure the availability and reliability of data systems. Proactively identifying and resolving issues and investigating data-related incidents or anomalies.
- Keeping up with the latest trends and technologies in the data engineering field. Evaluating and recommending new tools, frameworks, and technologies to improve data engineering processes and efficiency.
Preferred Qualifications
No preferred qualifications provided.
