Staff Software Engineer- Data Ingestion

Lever, Inc.

United States

19 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Experienced

Experience required

4 years minimum

Working hours

Regular working hours

Job source

jobs.lever.co

Tech stack

Java (Programming Language) Amazon Web Services Microsoft Azure Batch Processing C Sharp (Programming Language) C++ (Programming Language) Cloud Computing Databases Continuous Integration Data Files Extract Transform Load (ETL) Data Security

+26 more

Data Warehousing Database Queries Shard (Database Architecture) Distributed Systems Monitoring of Systems Python (Programming Language) Performance Tuning Query Optimization Standard Sql Scala (Programming Language) SQL Databases Google Cloud Data Ingestion Snowflake Apache Spark Caching Indexer Containerization Kubernetes Information Technology Web Technologies Tools for Reporting Data Pipelines Docker Databricks Golang

Requirements

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)., * 8+ years experience building highly scalable and reliable infrastructure.
Expertise in designing, optimizing, and orchestrating robust data pipelines (ETL/ELT) and ingestion systems for large-scale, real-time, and batch processing.
Experience managing data warehouses (e.g., Snowflake, Redshift) and leveraging analytics tools (e.g., Spark, SQL, Python, Databricks).
Hands-on experience with containerization (Docker, Kubernetes), CI/CD pipelines, and distributed architectures (event-driven, in-memory computing).
Deep proficiency with modern database systems, including replication, sharding, partitioning, indexing, and caching strategies for high-performance query optimization.
Strong understanding of data security, governance, and compliance principles.
Experience with infrastructure monitoring, performance optimization, and active participation in architecture reviews.

Benefits & conditions

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.