• Build and maintain data pipelines that ingest real-time positioning and telemetry data from tracking hardware. • Design data models and schemas that support real-time event detection (e.g., correlating two tracked objects' positions to infer a pickup or handoff event). • Implement data quality checks, monitoring, and alerting to catch pipeline failures or data drift early. • Optimize pipelines for near-real-time processing at the latency the product requires. • Work with structured and semi-structured data coming from external hardware and API integrations. • Collaborate with software engineers to ensure data is accessible, reliable, and supports platform features. • Support deployment, monitoring, and troubleshooting of data pipelines in production.

Role Description - Strong experience in Python programming. - Hands-on expertise with PySpark for large-scale data processing. - Experience with Google Cloud Platform (GCP) services: - BigQuery - Dataproc - Dataflow - Cloud Storage - Pub/Sub - Cloud Composer (Airflow) - Strong SQL skills and experience with relational databases. - Experience building batch and real-time data pipelines. - Good understanding of Data Lake and Data Warehouse concepts. - Knowledge of distributed computing and Spark optimization techniques. - Experience with Git, CI/CD pipelines, and Agile methodologies. Company Description At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. - At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus. - Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. - Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself. - We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. - We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. - All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.

Role Description We're looking for a mid-level Data Engineer to build and maintain the data infrastructure behind a real-time facility navigation and asset-tracking platform for industrial and logistics sites. The platform ingests live positioning and telemetry data from BLE-based tracking hardware, correlates it into events (e.g., asset pickups, movement, dwell time), and transforms it into reliable, actionable data for downstream applications and analytics. You'll be responsible for designing, building, and optimizing the data pipelines that power the platform, ensuring high data quality, scalability, and near-real-time processing. You'll work closely with other engineers on a small, fast-moving team. - Build and maintain data pipelines that ingest real-time positioning and telemetry data from tracking hardware. - Design data models and schemas that support real-time event detection (e.g., correlating two tracked objects' positions to infer a pickup or handoff event). - Implement data quality checks, monitoring, and alerting to catch pipeline failures or data drift early. - Optimize pipelines for near-real-time processing at the latency the product requires. - Work with structured and semi-structured data coming from external hardware and API integrations. - Collaborate with software engineers to ensure data is accessible, reliable, and supports platform features. - Support deployment, monitoring, and troubleshooting of data pipelines in production. Qualifications - 2–5 years of professional Data Engineering or Software Engineering experience. - Hands-on experience building and maintaining data pipelines (batch or streaming/ETL-ELT). - Solid SQL and relational database skills; comfort with event or time-series data is a plus. - Experience integrating with third-party APIs and webhooks. - Comfortable working with real-time or near-real-time data processing requirements. - Experience with a major cloud platform (Azure preferred). - Strong communication skills and comfort working in a small, cross-functional, fast-moving team. Nice to Have - Experience with geospatial or positioning data (GPS, BLE, RTLS). - Experience with message queues or event streaming (e.g., Kafka, Azure Service Bus). - Background in industrial, logistics, or IoT domains. Requirements - Remote work (Portugal-based). - Estimated project duration: 19 weeks. - Full-time schedule: 8 hours per day, with mandatory availability for at least 4 hours. - Freelance / Service Agreement. - Hourly Rate: 31€-33€.

• Design, develop, and maintain scalable data pipelines using Microsoft Azure Fabric, Databricks, and Azure data services. • Build and optimize robust ETL/ELT processes for structured, semi-structured, and unstructured data across enterprise environments. • Collaborate with business stakeholders to understand data requirements and translate them into scalable technical solutions. • Develop and maintain reliable data integration workflows that ensure data accuracy, consistency, and availability. • Optimize data processing pipelines for performance, scalability, cost efficiency, and operational reliability. • Leverage AI-assisted development tools and modern engineering practices to accelerate solution delivery and improve code quality. • Monitor, troubleshoot, and resolve production data pipeline issues while proactively identifying opportunities for automation and optimization. • Work closely with architects, analysts, developers, and cross-functional teams throughout the project lifecycle to deliver high-quality data solutions. • Implement best practices for data engineering, governance, documentation, testing, and operational support. • Take ownership of project deliverables by ensuring quality, meeting timelines, communicating risks proactively, and continuously improving engineering processes.