Senior Data Engineer

Your Mission

This position bridges the gap between business requirements and technical implementation. It encompasses the design of data pipelines, integrations, and storage solutions (databases, data warehouses, and big data).

What To Expect

Project Leadership

• Collaborate and coordinate with multiple departments, stakeholders, partners, and external vendors to deliver scalable and reliable data solutions.
• Proactively identify technical roadblocks and implement mitigation strategies to keep development on track.
• Assess data implementation procedures and ensure compliance with internal policies, security standards, and external regulatory requirements.
• Produce technical design documentation, implementation specifications, operational runbooks, deployment guides, and knowledge transfer materials.

Technology Roadmap Development

• Translate business requirements into technical specifications, including data streams, integrations, transformations, databases, data warehouses, and big data solutions.
• Contribute to the design and implementation of data architecture, data models, metadata standards, and security controls in alignment with enterprise architecture and governance guidelines.
• Design and implement end-to-end data pipelines for smart factory use cases, including Industrial IoT (IIoT) sensor data, machine events, production traceability, quality inspection data, logistics data, and enterprise application data.
• Design and implement scalable Lakehouse data solutions supporting analytics, business intelligence, AI/ML, Digital Twin, and enterprise reporting use cases.

Technical Leadership

• Evaluate applications, databases, and source systems to identify integration patterns, performance bottlenecks, data quality issues, and opportunities for standardization and optimization.
• Develop and maintain batch, streaming, and event-driven data pipelines using modern data engineering frameworks and cloud-native technologies.
• Design and implement data quality validation, reconciliation, monitoring, and observability mechanisms to ensure trusted, reliable, and production-ready datasets.
• Implement DataOps practices, including version control, automated testing, CI/CD deployment, monitoring, incident management, and operational support for data pipelines.
• Optimize data pipelines, storage structures, and query performance to improve scalability, reliability, and operational efficiency.
• Establish engineering best practices for pipeline development, testing, deployment, monitoring, observability, and operational reliability, and provide guidance to junior engineers.

What You'll Bring

• Bachelor's Degree in Computer Science, Information Technology, Computer Engineering, Data Engineering, or a related discipline.
• At least 8–10 years of experience in Data Engineering, Data Warehousing, ETL/ELT development, and large-scale data platform implementations.
• Strong experience in designing and implementing production-grade data pipelines using batch, streaming, and event-driven architectures.
• Strong programming skills in Python and SQL for data engineering, automation, and data transformation workloads.
• Hands-on experience with Apache Spark, Hadoop, Hive, Airflow, Kafka, MQTT, CDC technologies, REST APIs, and Industrial IoT (IIoT) integration patterns.
• Strong understanding of database architecture, data modeling, semantic modeling, indexing, partitioning, query optimization, and data lifecycle management.
• Experience with enterprise database technologies such as MySQL, PostgreSQL, Oracle, SQL Server, Tibero, or equivalent platforms.
• Hands-on experience with modern cloud-based data platforms such as Databricks, Snowflake, BigQuery, Redshift, Synapse, or equivalent technologies.
• Experience developing Lakehouse architectures and working with Delta Lake, Apache Iceberg, Hudi, or similar open table formats is an advantage.
• Experience with cloud platforms such as AWS, Azure, or GCP, including designing, deploying, and managing cloud-native data solutions.
• Experience implementing DataOps practices, CI/CD pipelines, Docker, infrastructure automation, monitoring, and operational support for data platforms.
• Good understanding of data governance, metadata management, data quality frameworks, data lineage, and data security principles.
• Experience working with manufacturing, Industrial IoT (IIoT), MES, PLC, SCADA, traceability, quality, logistics, equipment, maintenance, or shop-floor systems is highly preferred.
• Strong analytical, problem-solving, and stakeholder management skills, with the ability to communicate complex technical concepts to both technical and non-technical audiences.
• Experience working in cross-functional Agile delivery teams and supporting large-scale enterprise transformation initiatives is an advantage.