DeWinter Group
Overview
Data Engineer | Job Type: Full time/Perm OR contract to hire; Contract Length: 3-6 months if contract to hire; Target Start Date: ASAP (ideally in next 2-3 weeks); Work Location/Structure: Remote. About the Opportunity
Our client, a leader in the Compliance sector, is seeking a skilled
Senior
Data Engineer
to help design, build, and maintain the analytical data layer for their platform. This role will own the process of transforming massive volumes of operational data into clean, structured, and accessible datasets for analytics, reporting, and machine learning workloads. You’ll work closely with our data science and product teams to ensure that data is optimized for insight generation and business intelligence across a 5+ terabyte data environment. This position is ideal for someone who thrives in a fast-paced startup, enjoys solving data scaling challenges, and wants to work on real-world anti–money laundering and fraud detection datasets. This is a high-impact role that requires a self-motivated professional who can hit the ground running and deliver results quickly. Key Responsibilities & Deliverables
This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include: Build and maintain scalable data pipelines using Apache Spark, Azure Databricks, and Azure Fabric. Implement and maintain data lakehouse architecture (bronze, silver, gold tiers) to ensure structured, auditable data flow. Develop and manage ETL processes to move data between operational systems, Cosmos DB, and analytical layers. Create and optimize data models to support analytics, dashboards, and ML workloads. Collaborate with data scientists to prepare datasets for AI/ML model training and experimentation. Design and implement monitoring and data quality validation frameworks. Support business intelligence by creating visualizations and reports (Power BI or similar). Partner with cloud engineering to optimize storage and compute costs in Azure. Ensure all data flows comply with governance, privacy, and compliance requirements. Required Skills & Experience
We are looking for someone with a proven track record of successful contract engagements. The ideal candidate will have: 3–6 years of experience as a Data Engineer or equivalent analytics engineering role. Strong proficiency with Apache Spark (PySpark preferred) and Azure Databricks. Experience with Azure Fabric, Azure Data Lake, and Cosmos DB (or other NoSQL databases). Hands-on experience with ETL/ELT tools and designing scalable data lakehouse architectures. Working knowledge of Power BI or equivalent BI/reporting tools. Strong understanding of medallion architecture (bronze/silver/gold layers). Solid Python skills and familiarity with Jupyter Notebooks for exploratory analysis. Understanding of data governance, lineage, and drift detection practices. Excellent communication skills and ability to translate technical insights for business stakeholders. Experience with AI or ML pipeline support (data prep for training, feature engineering) (Preferred). Familiarity with Terraform and Infrastructure-as-Code for data environments (Preferred). Background in financial compliance, gaming, or anti-money laundering data (Preferred).
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Data Engineer | Job Type: Full time/Perm OR contract to hire; Contract Length: 3-6 months if contract to hire; Target Start Date: ASAP (ideally in next 2-3 weeks); Work Location/Structure: Remote. About the Opportunity
Our client, a leader in the Compliance sector, is seeking a skilled
Senior
Data Engineer
to help design, build, and maintain the analytical data layer for their platform. This role will own the process of transforming massive volumes of operational data into clean, structured, and accessible datasets for analytics, reporting, and machine learning workloads. You’ll work closely with our data science and product teams to ensure that data is optimized for insight generation and business intelligence across a 5+ terabyte data environment. This position is ideal for someone who thrives in a fast-paced startup, enjoys solving data scaling challenges, and wants to work on real-world anti–money laundering and fraud detection datasets. This is a high-impact role that requires a self-motivated professional who can hit the ground running and deliver results quickly. Key Responsibilities & Deliverables
This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include: Build and maintain scalable data pipelines using Apache Spark, Azure Databricks, and Azure Fabric. Implement and maintain data lakehouse architecture (bronze, silver, gold tiers) to ensure structured, auditable data flow. Develop and manage ETL processes to move data between operational systems, Cosmos DB, and analytical layers. Create and optimize data models to support analytics, dashboards, and ML workloads. Collaborate with data scientists to prepare datasets for AI/ML model training and experimentation. Design and implement monitoring and data quality validation frameworks. Support business intelligence by creating visualizations and reports (Power BI or similar). Partner with cloud engineering to optimize storage and compute costs in Azure. Ensure all data flows comply with governance, privacy, and compliance requirements. Required Skills & Experience
We are looking for someone with a proven track record of successful contract engagements. The ideal candidate will have: 3–6 years of experience as a Data Engineer or equivalent analytics engineering role. Strong proficiency with Apache Spark (PySpark preferred) and Azure Databricks. Experience with Azure Fabric, Azure Data Lake, and Cosmos DB (or other NoSQL databases). Hands-on experience with ETL/ELT tools and designing scalable data lakehouse architectures. Working knowledge of Power BI or equivalent BI/reporting tools. Strong understanding of medallion architecture (bronze/silver/gold layers). Solid Python skills and familiarity with Jupyter Notebooks for exploratory analysis. Understanding of data governance, lineage, and drift detection practices. Excellent communication skills and ability to translate technical insights for business stakeholders. Experience with AI or ML pipeline support (data prep for training, feature engineering) (Preferred). Familiarity with Terraform and Infrastructure-as-Code for data environments (Preferred). Background in financial compliance, gaming, or anti-money laundering data (Preferred).
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