Senior Data Engineer

About the role

Osol is looking for a hands-on Senior Data Engineer to build the engineering backbone of its Data & Analytics practice. The role proves and standardises how data is engineered through proofs of concept, pilots and reusable accelerators, then applies those standards to client engagements, industrial data platforms and the Industrial AI program delivered in group manufacturing plants. It is a senior individual contributor role that grows into mentoring engineers and leading a data engineering team.

Key responsibilities

• Define the company's data engineering methodology, reference patterns (lakehouse layering, CDC, streaming), coding and testing standards, definition of done and estimation guides.
• Plan and run proofs of concept and pilots across cloud, on-premises and hybrid platforms, publishing evidence-based comparisons of capability, performance, cost and operability.
• Build metadata-driven ingestion frameworks, data quality rule libraries, CI/CD and infrastructure-as-code templates, and demo environments for presales and partners.
• Build pipelines for structured, semi-structured and unstructured data, SAP and Oracle ERP, APIs, files, documents, logs, event streams and IoT sensors, in batch, micro-batch and real time.
• Implement change data capture and event-driven integration, handling late, duplicated and out-of-order data and backfilling long histories.
• Build in security, governance, data quality checks, reliability and cost control, and operate pipelines with orchestration, automated testing, CI/CD and SLA-based monitoring.
• Build portable, reusable pipelines that run the same on premises and in the cloud, keeping each client's data separate.
• Deliver analysis-ready datasets and feature pipelines for use cases such as predictive maintenance, moving them from pilot to production.
• Support presales with technical discovery, sizing and estimates, and lead technical delivery through design and code reviews.
• Acquire data from plant historians, control, maintenance and laboratory systems via read-only access in segmented plant networks, buffering through outages and backfilling history.

Required profile

• Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, Engineering or a related discipline.
• 7-10 years in data engineering, data integration or data warehousing, including 3+ years as a technical lead or lead engineer.
• Has led the technical build of at least one data platform or major integration program reaching production.
• Multi-industry experience strongly preferred; industrial or OT data experience is a plus.
• Data engineering certification on a major platform (e.g. Databricks, Microsoft Fabric, Snowflake, AWS or Google Cloud) is an advantage.

Required skills

• Deep production-grade expertise in at least one modern data platform and one cloud provider stack, hands-on experience in several others in the same category.
• Expert SQL, strong Python and Apache Spark (PySpark / Spark SQL).
• Proven batch and real-time pipelines with Kafka or a cloud equivalent, and change data capture.
• Dimensional models, Data Vault and lakehouse layer design.
• Git, CI/CD, infrastructure as code, and pipeline monitoring.