Position Summary
We are seeking a highly skilled Data Engineer to support enterprise AI/ML and Advanced Analytics initiatives by building scalable, reliable, and high-performance data platforms and pipelines. This role will focus on designing and engineering modern data solutions that enable machine learning, advanced analytics, and AI-driven decision-making. The ideal candidate has strong expertise in data architecture, cloud platforms, large-scale data processing, and data engineering best practices. The successful candidate will build and optimize data ecosystems that empower data scientists, analysts, and business teams to generate actionable insights and operational value.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines supporting AI/ML and analytics workloads.
- Build and optimize batch and real-time data ingestion frameworks.
- Develop data integration solutions across multiple internal and external data sources.
- Engineer reliable datasets and feature stores that support machine learning model development.
- Implement data transformation, cleansing, enrichment, and validation processes.
- Design and maintain modern data lake, lakehouse, and data warehouse architectures.
- Ensure data quality, integrity, security, and governance standards are met.
- Optimize data processing performance, scalability, and operational efficiency.
- Collaborate with Data Scientists and Analysts to support feature engineering and model deployment requirements.
- Enable MLOps and ML platform capabilities to support model operationalization.
- Implement monitoring, observability, and operational support processes for data platforms.
- Maintain documentation, data lineage, and metadata management standards.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, Data Engineering, or a related field.
- 4 years of experience in Data Engineering, Data Platforms, or Analytics Engineering.
- Strong proficiency in Python, SQL, Spark, Scala, or equivalent technologies.
- Experience building cloud-native data solutions using Azure, AWS, or GCP.
- Experience with ETL/ELT frameworks and large-scale data processing.
- Knowledge of distributed data processing technologies and modern data architectures.
- Experience with data lake, warehouse, and lakehouse platforms.
- Understanding of data governance, security, and data quality practices.
- Strong collaboration and problem-solving skills.
Preferred Qualifications
- Experience supporting AI/ML platforms and Data Science workloads.
- Experience with Databricks, Snowflake, Synapse, Fabric, BigQuery, or similar platforms.
- Familiarity with feature stores, MLOps, and model deployment pipelines.
- Experience with real-time streaming technologies such as Kafka or Event Hubs.
- Experience in enterprise-scale data ecosystems.
Key Competencies
- Data Engineering Excellence: Build scalable, reliable, and high-performance data solutions.
- Cloud & Modern Data Platforms: Design and optimize cloud-native architectures supporting AI and analytics.
- Data Pipeline Development: Develop robust ingestion, transformation, and orchestration frameworks.
- AI/ML Enablement: Create data foundations that accelerate machine learning and advanced analytics.
- Data Quality & Governance: Ensure trusted, secure, and compliant enterprise data assets.
- Operational Excellence: Drive reliability, scalability, monitoring, and maintainability across data platforms.
Success Profile
The ideal candidate:
- Builds scalable data ecosystems that power analytics and AI initiatives.
- Ensures data is accessible, trusted, and ready for advanced analytics.
- Partners effectively with Data Scientists, Architects, and Product teams.
- Improves performance, reliability, and efficiency of data platforms.
- Enables faster insight generation and AI innovation.
- Delivers robust engineering solutions that create measurable business value.