Must Haves:
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field
- 6+ years of experience in data engineering or data platform development, injest to transition models
- Extremely strong Databricks Experience
- Familiar with using Genie for AI – creating dev pipelines to tetsing
- Drtong Azure
- Strong proficiency in SQL and Python
- Experience building ETL/ELT pipelines and data models
- Familiarity with data warehousing concepts and medallion architecture
Preferred Qualifications
- Experience supporting AI/ML workflows or data science teams
- Knowledge of manufacturing, supply chain, or IoT data environments
- Experience with Informatica, Databricks, or streaming technologies (Kafka/Event Hub)
- Understanding of data governance frameworks and data quality tools
- Exposure to Power BI or modern BI tools
- Manufacturing or similar industry experience
Day to Day:
My client is looking for a Sr. Data Engineer to design, build, and scale modern data pipelines and AI-ready data platforms that drive operational excellence, manufacturing insights, and advanced analytics across the enterprise.
This role will be a key contributor to client’s data modernization initiative, enabling the transition to a cloud-based, scalable architecture that supports AI/ML, IoT data integration, and enterprise reporting. The ideal candidate combines strong data engineering fundamentals with experience preparing data for AI use cases such as predictive maintenance, demand forecasting, and process optimization.
Key Responsibilities
Data Engineering & Platform Development
- Design and build scalable ELT/ETL pipelines using cloud-native technologies (e.g., Snowflake, Azure, Databricks)
- Develop and maintain bronze/silver/gold data layers (medallion architecture) for analytics and AI consumption
- Integrate data from ERP, manufacturing systems, IoT sensors, and third-party platforms into a unified data environment
- Ensure high data quality, reliability, and performance across pipelines
AI & Advanced Analytics Enablement
- Engineer datasets optimized for machine learning and AI models
- Partner with data scientists and analytics teams to support predictive maintenance, demand forecasting, and anomaly detection use cases
- Implement data pipelines that support real-time or near-real-time analytics where applicable
- Enable feature engineering and model-ready datasets
Data Integration & Modernization
- Support migration from legacy platforms (e.g., SQL Server/SSAS) to modern cloud data platforms
- Build and maintain integrations using tools such as Informatica, APIs, and streaming frameworks
- Contribute to architecture patterns that enable scalable and reusable data products
Data Governance, Security & Quality
- Implement and enforce data governance standards, lineage, and metadata management
- Ensure compliance with data security and privacy policies
- Monitor and improve data quality and pipeline performance metrics
Collaboration & Business Alignment
- Partner with business stakeholders (Manufacturing, Supply Chain, Sales) to understand data needs
- Work closely with analytics engineers, BI developers, and SMEs to deliver trusted data assets
- Translate business requirements into scalable data solutions
Success Metrics (First 12 Months)
- Delivery of production-ready pipelines supporting key business domains
- Reduction in data latency and improvement in data quality
- Enablement of at least 2–3 high-value AI/analytics use cases
- Contribution to platform modernization milestones