
Lead Enterprise Data Engineer
5 days ago
This position involves working with data scientists and architects to translate prototypes into scalable solutions. The role requires designing and building robust, scalable, and high-quality data pipelines that support analytics and reporting needs.
Key Responsibilities:- Designing & Developing Data Pipelines:
- Integration of structured and unstructured data from various sources into data lakes and warehouses.
- Build and maintain scalable ETL/ELT pipelines for batch and streaming data using Azure Data Factory, Databricks, Snowflake, and Azure SQL Server.
- Cloud Platform Engineering:
- Operationalizing data solutions on cloud platforms, integrating services like Azure, Snowflake, and third-party technologies.
- Managing environments, performance tuning, and configuration for cloud-native data solutions.
- Data Modeling & Architecture:
- Applying dimensional modeling, star schemas, and data warehousing techniques to support business intelligence and machine learning workflows.
- Collaborating with solution architects and analysts to ensure models meet business needs.
- Agile Delivery:
- Working closely with cross-functional teams including product owners, architects, and business stakeholders to translate requirements into technical solutions.
- Participating in Agile ceremonies, sprint planning, and DevOps practices for continuous integration and deployment.
- 7+ years of data engineering or design experience, designing, developing, and deploying scalable enterprise data analytics solutions from source system through ingestion and reporting.
- 5+ years of experience in ML Lifecycle using Azure Kubernetes service, Azure Container Instance service, Azure Data Factory, Azure Monitor, Azure DataBricks building datasets, ML pipelines, experiments, logging, and monitoring.
- 5+ years of experience in data engineering using Snowflake.
- Experience in designing, developing & scaling complex data & feature pipelines feeding ML models and evaluating their performance.
- Proficiency in SQL and any one other programming language (e.g., R, Python, C++, Minitab, SAS, Matlab, VBA).
- Strong experience with cloud platforms (AWS, Azure, etc.) and containerization technologies (Docker, Kubernetes).
- Experience with CI/CD tools such as GitHub Actions, GitLab, Jenkins, or similar tools.
- Familiarity with security best practices in DevOps and ML Ops.
- Experience in developing and maintaining APIs (e.g.: REST).
- Agile/Scrum operating experience using Azure DevOps.
A degree is not required, as long as the candidate has the right skillset and can commit to the work/projects assigned.
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