Lead Software and Data Engineer
Posted Updated
Summary
A lead engineering role designing and building enterprise software and data platforms — data warehouses/lakes, ETL/ELT pipelines, APIs, and microservices — using Python, cloud platforms, and Databricks to drive supply-chain digital transformation. The hire also leads technical teams, sets DevOps/CI-CD best practices, and mentors engineers.
(Job ID: 1678283)
Job Summary: Lead the development of scalable software and data platforms that drive digital transformation across supply chain and enterprise operations.
Responsibilities:
Lead the design, development, and implementation of enterprise software applications and data platforms to support business operations and digital transformation initiatives.
Architect and manage scalable data infrastructure, including data warehouses, data lakes, and modern data platforms to support analytics and operational reporting.
Develop and maintain data pipelines, ETL/ELT processes, and data integration frameworks to ensure data accuracy, accessibility, and governance.
Design and implement backend systems, APIs, and microservices to support business-critical applications and platform services.
Drive the adoption of modern data engineering practices using cloud-based technologies and platforms such as Databricks.
Collaborate with business stakeholders to gather requirements and translate operational needs into scalable software and data solutions.
Lead system integration projects involving enterprise applications, ERP systems, warehouse management systems, and third-party platforms.
Establish best practices for software engineering, data architecture, DevOps, CI/CD, and platform governance.
Mentor and provide technical leadership to software engineers, data engineers, and cross-functional development teams.
Monitor system performance, reliability, security, and scalability while continuously improving platform capabilities.
Support technology roadmap planning and recommend innovative solutions that deliver measurable business value.
Requirements:
Degree in Computer Science, Information Technology, Data Science, Software Engineering, or a related discipline.
At least 5 years of experience in software engineering, application development, data engineering, or related fields.
Proven experience leading technical teams and delivering complex software or data platform projects.
Strong proficiency in Python for application development, automation, data processing, and system integration.
Hands-on experience designing and implementing data warehouses, data lakes, and modern data platforms.
Experience with Databricks, Spark, or similar big data and analytics platforms.
Strong knowledge of database technologies, including SQL and NoSQL databases.
Experience developing APIs, backend services, microservices, and enterprise applications.
Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
Knowledge of containerization technologies, DevOps practices, CI/CD pipelines, and software deployment methodologies.
Experience integrating enterprise systems such as ERP, WMS, CRM, or other business applications.
Strong understanding of data governance, data quality, security, and scalability best practices.
Experience in supply chain, logistics, procurement, manufacturing, or related operational environments is an added advantage.
Experience in Artificial Intelligence (AI), Machine Learning (ML), predictive analytics, or advanced data modelling is an added advantage.
Knowledge of time-series forecasting, optimization algorithms, or MLOps practices is an added advantage.
Strong stakeholder management, communication, and problem-solving skills.
Job Summary: Lead the development of scalable software and data platforms that drive digital transformation across supply chain and enterprise operations.
Responsibilities:
Lead the design, development, and implementation of enterprise software applications and data platforms to support business operations and digital transformation initiatives.
Architect and manage scalable data infrastructure, including data warehouses, data lakes, and modern data platforms to support analytics and operational reporting.
Develop and maintain data pipelines, ETL/ELT processes, and data integration frameworks to ensure data accuracy, accessibility, and governance.
Design and implement backend systems, APIs, and microservices to support business-critical applications and platform services.
Drive the adoption of modern data engineering practices using cloud-based technologies and platforms such as Databricks.
Collaborate with business stakeholders to gather requirements and translate operational needs into scalable software and data solutions.
Lead system integration projects involving enterprise applications, ERP systems, warehouse management systems, and third-party platforms.
Establish best practices for software engineering, data architecture, DevOps, CI/CD, and platform governance.
Mentor and provide technical leadership to software engineers, data engineers, and cross-functional development teams.
Monitor system performance, reliability, security, and scalability while continuously improving platform capabilities.
Support technology roadmap planning and recommend innovative solutions that deliver measurable business value.
Requirements:
Degree in Computer Science, Information Technology, Data Science, Software Engineering, or a related discipline.
At least 5 years of experience in software engineering, application development, data engineering, or related fields.
Proven experience leading technical teams and delivering complex software or data platform projects.
Strong proficiency in Python for application development, automation, data processing, and system integration.
Hands-on experience designing and implementing data warehouses, data lakes, and modern data platforms.
Experience with Databricks, Spark, or similar big data and analytics platforms.
Strong knowledge of database technologies, including SQL and NoSQL databases.
Experience developing APIs, backend services, microservices, and enterprise applications.
Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
Knowledge of containerization technologies, DevOps practices, CI/CD pipelines, and software deployment methodologies.
Experience integrating enterprise systems such as ERP, WMS, CRM, or other business applications.
Strong understanding of data governance, data quality, security, and scalability best practices.
Experience in supply chain, logistics, procurement, manufacturing, or related operational environments is an added advantage.
Experience in Artificial Intelligence (AI), Machine Learning (ML), predictive analytics, or advanced data modelling is an added advantage.
Knowledge of time-series forecasting, optimization algorithms, or MLOps practices is an added advantage.
Strong stakeholder management, communication, and problem-solving skills.
Skills
- Accessibility
- AI
- Analytics
- API
- Automation
- AWS
- Azure
- CI/CD
- Cloud
- Containerization
- CRM
- Data Engineering
- Data Governance
- Data Modeling
- Data Pipelines
- Data Quality
- Data Science
- Databricks
- DevOps
- ELT
- ERP
- ETL
- GCP
- Machine Learning
- Microservices
- MLOps
- NoSQL
- Predictive Analytics
- Python
- Spark
- SQL
- Stakeholder Management
- Time Series