• Build and support non-interactive (batch, distributed) & real-time data pipelines • Build fault-tolerant, self-healing, adaptive, and highly accurate data computational pipelines • Provide consultation and lead implementation of complex programs • Develop and maintain documentation for assigned systems and projects • Tune queries over billions of rows of data in a distributed query engine • Perform root cause analysis for software/business process issues • Implement and maintain dbt transformation models, CI pipelines, data contracts • Build and monitor data quality gates and freshness SLOs • Optimize BigQuery cost and performance with query tuning, storage design • Implement platform hardening controls including retries, dead-letter queues

Role Description As a Senior Big Data Engineer, working within Placer.ai's Mobility Group, you will play a pivotal role in designing, developing, and maintaining the data infrastructure that powers our location analytics platform. Responsibilities - Data Pipeline Architecture and Development: Design, build, and optimize robust and scalable data pipelines to process, transform, and integrate large volumes of data from various sources into our analytics platform. - Data Quality Assurance: Implement data validation, cleansing, and enrichment techniques to ensure high-quality and consistent data across the platform. - Performance Optimization: Identify performance bottlenecks and optimize data processing and storage mechanisms to enhance overall system performance and reduce latency. - Cloud Infrastructure: Work extensively with cloud-based technologies (GCP and AWS) to design and manage scalable data infrastructure. - Collaboration: Collaborate with cross-functional teams including Data Analysts, Data Scientists, Product Managers, and Software Engineers to understand requirements and deliver solutions that meet business needs. - Data Governance: Implement and enforce data governance practices, ensuring compliance with relevant regulations and best practices related to data privacy and security. - Monitoring and Maintenance: Monitor the health and performance of data pipelines, troubleshoot issues, and ensure high availability of data infrastructure. - Mentorship: Provide technical guidance and mentorship to junior data engineers, fostering a culture of learning and growth within the team. Qualifications - Bachelor's or Master's degree in Computer Science, Engineering, or a related field. - 5+ years of professional experience in software development, with at least 3 years as a Data Engineer. - Spark expertise (mandatory): Strong proficiency in Apache Spark, including hands-on experience with building data processing applications and pipelines using Spark's core libraries. - PySpark/Scala (Mandatory): Proficiency in either PySpark (Python API for Spark) or Scala for Spark development. - Data Engineering: Proven track record in designing and implementing ETL pipelines, data integration, and data transformation processes. - Cloud Platforms: Hands-on experience with cloud platforms such as AWS, GCP, or Azure. - SQL and Data Modeling: Solid understanding of SQL, relational databases, and data modeling. - Big Data Technologies: Familiarity with big data technologies beyond Spark, such as Hadoop ecosystem components, data serialization formats (Parquet, Delta), and distributed computing concepts. - Programming Languages: Proficiency in programming languages like Python, Java, or Scala. - ETL Tools and Orchestration: Familiarity with ETL tools and frameworks, such as Apache Airflow. - Problem-Solving: Strong analytical and problem-solving skills. - Collaboration and Communication: Effective communication skills and collaboration within cross-functional teams. - Geospatial Domain (Preferred): Prior experience in the geospatial or location analytics domain is a plus. Requirements - 5+ years of professional experience in software development, with at least 3 years as a Data Engineer. - Spark expertise (mandatory): Strong proficiency in Apache Spark. - PySpark/Scala (Mandatory): Proficiency in either PySpark or Scala. - Data Engineering: Proven track record in designing and implementing ETL pipelines. - Cloud Platforms: Hands-on experience with AWS, GCP, or Azure. - SQL and Data Modeling: Solid understanding of SQL and relational databases. - Big Data Technologies: Familiarity with big data technologies beyond Spark. - Programming Languages: Proficiency in Python, Java, or Scala. - ETL Tools and Orchestration: Familiarity with ETL tools and frameworks. - Problem-Solving: Strong analytical and problem-solving skills. - Collaboration and Communication: Effective communication skills. - Geospatial Domain (Preferred): Prior experience in the geospatial domain. Benefits - Join a rocketship! We are pioneers of a new market that we are creating. - Take a central and critical role at Placer.ai. - Work with, and learn from, top-notch talent. - Competitive salary. - Excellent benefits.

Role Description Our Client is hiring a Full‑Stack Data Engineer to strengthen their data foundation and support growing reporting needs across the organization. This is a hands‑on technical role for someone who thrives across the entire data lifecycle from building pipelines and transformations to delivering user‑facing dashboards and predictive insights. You will join a collaborative data team, working closely with engineering and analytics colleagues to ensure reliable data ingestion, efficient workflows, and clear reporting outputs that empower operational and clinical leadership. Key Responsibilities - Data Engineering - Design, build, and maintain data pipelines using GCP tools (BigQuery, Cloud Functions, Cloud Composer, Cloud Scheduler, Apache Beam, Airflow). - Clean, transform, and organize data from multiple sources. - Automate ETL/ELT workflows for reliability and scalability. - Support ingestion from APIs, spreadsheets, and internal systems. - Backend Development - Write Python and Bash scripts to process and automate data tasks. - Develop lightweight backend services and utilities to streamline internal processes. - Front‑End / Dashboards - Build and update dashboards in Looker Studio and D3.js. - Deliver clean, intuitive KPI reports for operations and leadership. - Support visualization needs across the Wellness Division. - Foundational ML / Predictive Work - Contribute to simple predictive modeling and forecasting tasks. - Prepare structured datasets for future machine learning initiatives. Qualifications - Must‑Have: - 2+ years in data engineering, data science, or software engineering. - Strong experience with GCP, including: - BigQuery - Cloud Functions / Cloud Run - Apache Beam & Airflow - Looker / Looker Studio Pro - Vertex AI (AutoML, LLM engineering) - Advanced Python (data processing, APIs, automation). - Experience building end‑to‑end pipelines (batch + streaming preferred). - Strong SQL skills for transformations and modeling. - Proven ability to develop dashboards, KPIs, and BI outputs. - Solid understanding of modern data architectures (lakehouse, warehousing, governance). - Nice‑to‑Have: - Exposure to healthcare or multi‑location environments. - Experience with EMR systems or similar platforms. - Familiarity with predictive analytics and ML workflows. Requirements - Location: South Africa (Remote) - Salary: $1500-$1800 - Time Zone: US Time Zone

Role Description As a Senior Data Engineer, you will drive the end-to-end development of our core data infrastructure through AI-native engineering. Championing our AI DevEx approach, you will orchestrate AI agents to rapidly build, scale, and refactor high-performance systems. We are looking for a technical expert who takes hands-on ownership of distributed, real-time architectures. Leveraging your deep data engineering expertise, you will construct the robust pipelines that power our next-generation data products. - Developing and driving the architecture of complex data systems that prioritize scalability, reliability, and long-term maintainability. - Designing and optimizing production-grade data pipelines, with a primary focus on high-throughput, real-time streaming. - Driving Spec-Driven Development (SDD) using OpenSpec or GitHub Spec Kit to create strict engineering contracts that ensure predictable, high-quality AI code generation. - Orchestrating agentic AI workflows with Claude Code and the Model Context Protocol (MCP) to rapidly build, refactor, and scale our data infrastructure. - Taking full accountability and ownership of system components, working in a self-sufficient manner to solve deep technical challenges. - Implementing rigorous testing and monitoring frameworks to ensure the integrity of mission-critical data. - Mentoring junior engineers and fostering a culture of technical excellence through open feedback and architectural reviews. Qualifications - 10+ years of professional experience, with 5+ years specifically focused on Data Engineering. - Deep technical expertise with Kafka (including Producers, Consumers, and Stream Processing) for building scalable, real-time architectures. - Strong experience with OLAP databases (expertise in Clickhouse is highly preferred). - Good fluency in Python and SQL for complex data manipulation, optimization, and system building. - Willingness to embrace highly agentic AI-assisted development, specifically using Claude Code to autonomously navigate codebases, execute multi-step engineering tasks, and accelerate development cycles while maintaining high code quality. - Strong hands-on experience with Docker, Kubernetes, and automated CI/CD pipelines. - Significant experience with open-source tools and technologies within the modern data stack. - Excellent communication skills and a desire to work in an environment based on transparency and feedback. - Fluency in English. Benefits - Global & Diverse Team: Work in a collaborative, multicultural environment. - Flexibility: Flexible working hours to help you shape your own work-life balance. - Remote-First: A truly remote-friendly environment. - Growth: Ongoing career development, training, and coaching. - Learning Time: Six dedicated days per year for learning and development. - Culture: Regular team events (in-person and virtual) and location-based social benefits.