Specialist-Data Engineering -IT

About the Role

The role leads the development, building, and testing of data solutions that deliver business value while meeting quality and technical requirements. It is accountable for end‑to‑end delivery of source data acquisition, complex transformation and orchestration pipelines, AI/ML engineering, and front‑end visualization, and requires strong communication and presentation skills. The incumbent collaborates directly with business stakeholders to deliver rapid, incremental outcomes and coordinates across diverse people, technologies, and environments. They manage robust data solutions aligned with quality measures such as availability, completeness, and accuracy, and assemble large, complex datasets that satisfy functional, non‑functional, and data protection standards. Technical responsibilities include building infrastructure and pipelines for optimal extraction, transformation, and loading from varied sources, deploying enterprise solutions across multiple data technology patterns, and designing scalable, reusable datasets. Solutions are documented through flowcharts, diagrams, code comments, data lineage, and metadata, while partnering with senior stakeholders, analysts, and data scientists to ensure requirements are met. • Lead development, testing, and delivery of end‑to‑end data solutions, including AI/ML engineering and visualization. • Design and orchestrate scalable data pipelines for extraction, transformation, and loading from diverse sources. • Collaborate with business stakeholders, functional analysts, and data scientists to define and meet requirements. • Implement robust data quality measures (availability, completeness, accuracy) and ensure compliance with data protection standards. • Develop data models using dimensional (Star, Snowflake, Data Vault) and transactional (3NF) approaches across OLTP, OLAP, NoSQL, and Big Data platforms. • Build and maintain infrastructure on Snowflake, Azure, and other cloud/on‑prem technologies, leveraging Spark, MPP warehouses, and containers. • Write production‑grade code in Python, Scala, SQL, and PL/SQL and create interactive dashboards with Power BI or similar tools. • Apply DevSecOps and CI/CD practices to automate pipeline deployment, monitoring, and observability. • Communicate complex technical concepts clearly and provide guidance to diverse audiences.

Key Responsibilities

• ▸data pipelines
• ▸ai/ml
• ▸data modeling
• ▸cloud infra
• ▸devsecops
• ▸dashboarding

What You Bring

Preferred domain knowledge spans engineering, digital, and Manufacturing/Industry 4.0, and the role demands conceptual thinking to address ill‑defined problems and consider long‑term impacts. Candidates should hold a graduate degree in Computer Science, Mathematics, Engineering, or equivalent, and bring at least ten years of data engineering experience with Snowflake, Palantir (preferred), Power BI, and cloud platforms such as Azure. Required technical expertise includes advanced experience with data storage and processing technologies (data lake, warehouse, time‑series, graph) on‑prem and cloud, proficiency in batch (ETL/ELT) and on‑demand (API, SQL) integration, cloud‑based storage and processing (Spark, MPP warehouses, containers), dimensional and transactional data modeling, and programming in Python, Scala, SQL, and PL/SQL. Additional skills cover data visualization, pipeline development with observability, DevSecOps, CI/CD, and the ability to convey complex information and advise diverse audiences.

Requirements

• ▸graduate degree
• ▸10+ years
• ▸snowflake
• ▸power bi
• ▸azure
• ▸python

Work Environment

Hybrid