Senior Data Engineer

Company Overview:
Cellebrite's (Nasdaq: CLBT) mission is to enable its global customers to protect and save lives by enhancing digital investigations and intelligence gathering to accelerate justice in communities around the world.

Cellebrite's AI-powered Digital Investigation Platform enables customers to lawfully access, collect, analyze and share digital evidence in legally sanctioned investigations while preserving data privacy. Thousands of public safety organizations, intelligence agencies and businesses rely on Cellebrite's digital forensic and investigative solutions-available via cloud, on-premises and hybrid deployments-to close cases faster and safeguard communities.

To learn more, visit us at ,  and find us on social media @Cellebrite.

Position Overview:
We're looking for a highly skilled and opinionated Senior Data Engineer to join our Data Platform team.

If you thrive in cloud-native, high-scale environments and enjoy full ownership of the data lifecycle - from design to deployment - this role is for you.

You'll work closely with data scientists, ML engineers, and product teams to build scalable, production-ready data infrastructure leveraging Apache Spark, AWS services, and Infrastructure-as-Code.

Key Responsibilities:

• Influence Data Architecture: Design scalable, secure data platforms using Spark (EMR), Glue, and AWS event-driven services.
• Develop Scalable Pipelines: Build batch & streaming ETL/ELT pipelines with Spark EMR, Athena, Iceberg, EKS, Lambda.
• Drive Innovation: Introduce patterns like data mesh, serverless analytics, and schema-aware pipelines.
• Cross-Team Collaboration: Work with ML, backend, and product teams to deliver data-powered solutions.
• Operational Excellence: Apply observability, cost control, performance tuning, and CI/CD automation using CloudWatch, Step Functions, Terraform/CDK.
• Security: Implement AWS best practices for IAM, encryption, compliance, and auditability.

Requirements:

• 8+ years of hands-on experience in data engineering, with proven responsibility for designing, developing, and maintaining large-scale, distributed data systems in cloud-native environments (preferably AWS).
• End-to-end ownership of complex data architectures – from data ingestion to processing, storage, and delivery in production-grade systems.
• Deep understanding of data modeling, data quality, and pipeline performance optimization.
• Apache Spark: Expertise in writing efficient Spark jobs using PySpark, with experience running workloads on AWS EMR and/or Glue for large-scale ETL and analytical tasks.
• AWS Services: Strong hands-on experience with S3, Lambda, Glue, Step Functions, Kinesis, and Athena – including building event-driven and serverless data pipelines.
• Solid experience in building and maintaining both batch and real-time (streaming) data pipelines and integrating them into production systems.
• Infrastructure as Code (IaC): Proficient in using Terraform, AWS CDK, or SAM to automate deployment and manage scalable data infrastructure.
• Python as a primary development language (with bonus points for TypeScript experience).
• Comfortable working in agile, fast-paced environments, with strong debugging, testing, and performance-tuning capabilities.

Nice to Have:

• AWS certifications (e.g., Solutions Architect, Data Engineer).
• Experience with ML pipelines or AI-enhanced analytics.
• Familiarity with data governance, data mesh, or self-service platforms.
• Domain experience in cybersecurity, law enforcement, or regulated industries.