About Powerfleet Powerfleet (Nasdaq: AIOT; JSE: PWR) is a global leader in the artificial intelligence of things (AIoT) software-as-a-service (SaaS) mobile asset industry. With more than 30 years of experience, Powerfleet unifies business operations through the ingestion, harmonization, and integration of data—regardless of source—and delivers actionable insights to help companies meet their strategic objectives around Safety, Compliance, Efficiency and Sustainability. Our people-first culture and relentless innovation empower customers to achieve measurable, sustainable business improvements. Powerfleet serves over 2.6 million subscribers across more than 48,000 customers in 120 countries, with commercial operations across every major continent. We’re looking for a seasoned Data / ML Engineer to join our Data Systems team. Someone who thrives on building robust, scalable data infrastructure and is comfortable bridging the gap between raw telemetry and production-grade analytical products. What You’ll Do Design, build, and maintain high-throughput ETL/ELT pipelines handling large volumes of real-time and batch telematics data Architect and evolve our data lake and warehouse infrastructure for reliability, scalability, and cost efficiency Build and maintain stream processing systems for low-latency data ingestion and transformation Collaborate with data scientists to operationalise ML models and integrate outputs into data products Own data quality, observability, and governance across the platform Contribute to architectural decisions across cloud infrastructure, storage, and compute layers Qualifications Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field with 8+ years of relevant experience, OR Master’s degree in a relevant field with 5+ years of experience Core Competencies Big Data & Processing: Advanced proficiency with Apache Spark (PySpark preferred); experience with high-volume batch and stream processing Streaming: Hands‑on experience with Apache Kafka or equivalent event‑streaming platforms; familiarity with event‑driven architectures and real‑time streaming (e.g. Azure Event Hubs) Data Lake & Lakehouse: Proven experience designing and hydrating data lakes and data warehouses; familiarity with open table formats (Apache Hudi, Delta Lake, or Iceberg) Databases: Strong working knowledge of PostgreSQL, MS SQL, Snowflake, and cloud‑native databases (e.g. AWS Aurora, Redshift, DynamoDB); expertise in data modelling, performance tuning, and warehousing methodologies (Kimball or Inmon) Languages: Python (primary); C# or Java (required); strong SQL; shell scripting (Bash/PowerShell) for automation; REST API development Cloud — AWS: Solid experience with AWS (S3, Glue, EMR, Lambda, Redshift, Step Functions, Lake Formation); multi‑cloud exposure is a plus DevOps & Tooling: CI/CD practices using Azure DevOps or GitHub Actions; Infrastructure-as-Code with Terraform and/or CloudFormation; database deployment automation with Liquibase Containerisation & Orchestration: Hands‑on experience with Docker, Kubernetes, and Apache Airflow for workflow orchestration Monitoring & Observability: Experience implementing monitoring, logging, and alerting for data systems using AWS CloudWatch, OpenTelemetry, or equivalent Data Governance & Security: Working knowledge of RBAC and IAM policies, data encryption, data lineage, and compliance best practices AI & Automation: Experience building or integrating automated ML pipelines and data workflows; familiarity with AI‑assisted tooling, LLM integration patterns, or agentic data processing is a strong plus Advantageous Experience with MLflow, SageMaker, Azure ML, or similar MLOps tooling Knowledge of data mesh or data product patterns Azure cloud familiarity (Databricks, ADLS, Azure SQL, Event Hubs) Telematics, IoT, warehouse/WMS, or time‑series data experience What We’re Looking For Someone who takes end‑to‑end ownership, from pipeline design to production reliability Communicates technical trade‑offs clearly to both engineering and non‑technical stakeholders Stays current with the data engineering landscape and applies new tools pragmatically Works well in a collaborative, cross‑functional environment Has a track record of delivering at scale, not just in proof‑of‑concept Brings a quality mindset and thinks about correctness, observability, and maintainability from day one Equal Employment Opportunity Statement Powerfleet is committed to maintaining a diverse, equitable, and inclusive workplace where all individuals are treated with dignity and respect. Employment decisions are based on qualifications, merit, and business needs. We do not discriminate or tolerate harassment on any protected basis under applicable laws in the countries where we operate, including characteristics such as race, ethnicity, nationality, religion, gender, gender identity or expression, sexual orientation, disability, or age. Powerfleet is an equal opportunity employer. We are committed to creating an inclusive and diverse workplace that reflects the rich diversity of South Africa. We are committed to the principles of the Employment Equity Act and Broad‑Based Black Economic Empowerment (BBBEE) and actively seek to empower suitably qualified candidates from designated groups. We believe that inclusion and diversity are key to innovation, growth, and shared success. #J-18808-Ljbffr