Senior Data Engineer

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November 26

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DigiCert

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Cybersecurity • IoT • Security

DigiCert is a global leader in providing high-assurance digital security solutions. It offers a wide range of services including TLS/SSL certificates, PKI (Public Key Infrastructure), and IoT (Internet of Things) security solutions. DigiCert's suite of products and services includes certificate lifecycle management, secure signing for code and documents, and devices across various industries such as healthcare, transportation, and smart cities. The company is focused on ensuring digital trust with advanced encryption and identity verification technologies, preparing organizations for the challenges of the quantum era. DigiCert is trusted by the majority of the Global 2000 companies for managing their digital trust needs.

1001 - 5000 employees

Founded 2003

🔒 Cybersecurity

🔐 Security

📋 Description

• Design, build, and optimize large-scale data ingestion, transformation, and streaming pipelines for DNS exhaust data collected from our global edge infrastructure. • Implement real-time and near real-time analytics pipelines using technologies such as Kafka, Flink, Spark Streaming, or Kinesis. • Design, develop, and maintain robust data models and warehouse structures (e.g., Snowflake, BigQuery, Redshift, ClickHouse) to support high-throughput analytical workloads. • Build tools and frameworks for data quality, validation, and observability across distributed, cloud-based systems. • Optimize data storage, retention, and partitioning strategies to balance cost and query performance. • Work with data visualization and analytics tools such as Grafana, Tableau, or similar platforms to surface operational metrics and data insights. • Collaborate with software and platform engineering teams to integrate real-time data into their services and customer-facing analytics. • Ensure data security and compliance in multi-tenant, high-volume cloud environments.

🎯 Requirements

• Four-year degree in IT, Computer Science, or a related field, or equivalent professional experience. • 7+ years of experience in data engineering or large-scale data infrastructure development. • Strong engineering background with experience building and operating distributed data systems capable of processing millions of transactions per second. • Proven experience with stream processing frameworks such as Kafka Streams, Flink, Spark Structured Streaming, or equivalent technologies. • Strong proficiency in languages supporting data pipeline development, such as Python, Go, or Scala. • Deep understanding of ETL/ELT design patterns and data warehouse technologies (e.g., Snowflake, BigQuery, Redshift, ClickHouse, Databricks). • Advanced SQL skills, including query optimization, schema design, and partitioning strategies for large-scale analytical datasets. • Extensive hands-on experience with cloud-based data services such as Athena, Glue, S3, Kinesis, and Lambda, as well as data lakehouse technologies like Apache Iceberg, Parquet, and Delta Lake.

🏖️ Benefits

• Generous time off policies • Top shelf benefits • Education, wellness and lifestyle support