Ardent MC
Data Engineer
Rol remoto de Data Engineering con fit claro de ubicación del candidato.
Publicado7 jul 2026
Países elegibles1 país aceptado
Señal de seniorityMiddle
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Estados Unidos
Resumen del rol
Data Engineer
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
Responsibilities and Duties:
- Data Engineering & Pipeline DevelopmentDesign, develop, and maintain scalable ETL/ELT pipelines to support enterprise data integration and analytics.Ingest, transform, and integrate data from diverse sources, including flat files, JSON, XML, Excel, REST APIs, graph databases, and other structured and unstructured data formats.Develop and optimize SQL and Python-based data processing solutions to support efficient data ingestion and transformation.Build and maintain reusable, scalable data workflows that support business intelligence, reporting, and advanced analytics.Data Platform ManagementLoad, manage, and optimize data within modern data platforms, including Databricks Unity Catalog and SQL Server Managed Instances.Support both batch and streaming data ingestion frameworks.Implement and maintain modern Lakehouse architecture solutions to improve scalability, performance, and accessibility.Monitor and optimize database and pipeline performance to ensure efficient processing and storage.Data Quality & GovernanceImplement data quality controls to ensure the accuracy, consistency, reliability, and integrity of enterprise data.Maintain data lineage and metadata to support governance and regulatory compliance.Apply enterprise data management (EDM) standards and best practices throughout the data lifecycle.Support data governance initiatives, including documentation, validation, and quality assurance activities.Collaboration & Analytics SupportCollaborate with cross-functional teams, including data analysts, software developers, architects, and business stakeholders, to understand data requirements and deliver effective solutions.Support analytical environments focused on fraud detection, anomaly detection, financial oversight, and other data-driven initiatives.Troubleshoot and resolve data pipeline, integration, and performance issues while continuously improving existing processes.
- Design, develop, and maintain scalable ETL/ELT pipelines to support enterprise data integration and analytics.
- Ingest, transform, and integrate data from diverse sources, including flat files, JSON, XML, Excel, REST APIs, graph databases, and other structured and unstructured data formats.
- Develop and optimize SQL and Python-based data processing solutions to support efficient data ingestion and transformation.
- Build and maintain reusable, scalable data workflows that support business intelligence, reporting, and advanced analytics.
- Load, manage, and optimize data within modern data platforms, including Databricks Unity Catalog and SQL Server Managed Instances.
- Support both batch and streaming data ingestion frameworks.
- Implement and maintain modern Lakehouse architecture solutions to improve scalability, performance, and accessibility.
- Monitor and optimize database and pipeline performance to ensure efficient processing and storage.
- Implement data quality controls to ensure the accuracy, consistency, reliability, and integrity of enterprise data.
- Maintain data lineage and metadata to support governance and regulatory compliance.
- Apply enterprise data management (EDM) standards and best practices throughout the data lifecycle.
- Support data governance initiatives, including documentation, validation, and quality assurance activities.
- Collaborate with cross-functional teams, including data analysts, software developers, architects, and business stakeholders, to understand data requirements and deliver effective solutions.
- Support analytical environments focused on fraud detection, anomaly detection, financial oversight, and other data-driven initiatives.
- Troubleshoot and resolve data pipeline, integration, and performance issues while continuously improving existing processes.
Data Engineering & Pipeline Development
- Design, develop, and maintain scalable ETL/ELT pipelines to support enterprise data integration and analytics.
- Ingest, transform, and integrate data from diverse sources, including flat files, JSON, XML, Excel, REST APIs, graph databases, and other structured and unstructured data formats.
- Develop and optimize SQL and Python-based data processing solutions to support efficient data ingestion and transformation.
- Build and maintain reusable, scalable data workflows that support business intelligence, reporting, and advanced analytics.
Data Platform Management
- Load, manage, and optimize data within modern data platforms, including Databricks Unity Catalog and SQL Server Managed Instances.
- Support both batch and streaming data ingestion frameworks.
- Implement and maintain modern Lakehouse architecture solutions to improve scalability, performance, and accessibility.
- Monitor and optimize database and pipeline performance to ensure efficient processing and storage.
Data Quality & Governance
- Implement data quality controls to ensure the accuracy, consistency, reliability, and integrity of enterprise data.
- Maintain data lineage and metadata to support governance and regulatory compliance.
- Apply enterprise data management (EDM) standards and best practices throughout the data lifecycle.
- Support data governance initiatives, including documentation, validation, and quality assurance activities.
Collaboration & Analytics Support
- Collaborate with cross-functional teams, including data analysts, software developers, architects, and business stakeholders, to understand data requirements and deliver effective solutions.
- Support analytical environments focused on fraud detection, anomaly detection, financial oversight, and other data-driven initiatives.
- Troubleshoot and resolve data pipeline, integration, and performance issues while continuously improving existing processes.
Requirements:
- Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field (or equivalent combination of education and experience).Minimum of 3 years of professional experience in data engineering or a related field.Demonstrated experience designing, building, and maintaining scalable ETL/ELT pipelines across multiple data sources.Strong proficiency in SQL and Python or equivalent technologies used for data engineering and transformation.Experience ingesting and transforming data from a variety of formats, including:Flat filesJSONXMLMicrosoft ExcelREST APIsGraph databasesAdditional structured and unstructured data sourcesExperience working with Databricks Unity Catalog, SQL Server Managed Instances, or comparable enterprise data platforms.Experience with streaming and batch ingestion frameworks and modern Lakehouse architecture.Strong understanding of data quality, data lineage, performance optimization, and enterprise data management principles.Familiarity with data governance, data quality, and data management practices aligned with Enterprise Data Management (EDM) standards.Experience supporting fraud detection, anomaly detection, financial oversight analytics, or similar analytical environments is preferred.Excellent analytical, problem-solving, and communication skills with the ability to collaborate effectively across technical and business teams.Due to the nature of the work we support, all candidates selected for this position must be willing to undergo a U.S. Government background investigation.
- Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field (or equivalent combination of education and experience).
- Minimum of 3 years of professional experience in data engineering or a related field.
- Demonstrated experience designing, building, and maintaining scalable ETL/ELT pipelines across multiple data sources.
- Strong proficiency in SQL and Python or equivalent technologies used for data engineering and transformation.
- Experience ingesting and transforming data from a variety of formats, including:Flat filesJSONXMLMicrosoft ExcelREST APIsGraph databasesAdditional structured and unstructured data sources
- Flat files
- JSON
- XML
- Microsoft Excel
- REST APIs
- Graph databases
- Additional structured and unstructured data sources
- Experience working with Databricks Unity Catalog, SQL Server Managed Instances, or comparable enterprise data platforms.
- Experience with streaming and batch ingestion frameworks and modern Lakehouse architecture.
- Strong understanding of data quality, data lineage, performance optimization, and enterprise data management principles.
- Familiarity with data governance, data quality, and data management practices aligned with Enterprise Data Management (EDM) standards.
- Experience supporting fraud detection, anomaly detection, financial oversight analytics, or similar analytical environments is preferred.
- Excellent analytical, problem-solving, and communication skills with the ability to collaborate effectively across technical and business teams.
- Due to the nature of the work we support, all candidates selected for this position must be willing to undergo a U.S. Government background investigation.
Details
- Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field (or equivalent combination of education and experience).
- Minimum of 3 years of professional experience in data engineering or a related field.
- Demonstrated experience designing, building, and maintaining scalable ETL/ELT pipelines across multiple data sources.
- Strong proficiency in SQL and Python or equivalent technologies used for data engineering and transformation.
- Experience ingesting and transforming data from a variety of formats, including:Flat filesJSONXMLMicrosoft ExcelREST APIsGraph databasesAdditional structured and unstructured data sources
- Flat files
- JSON
- XML
- Microsoft Excel
- REST APIs
- Graph databases
- Additional structured and unstructured data sources
- Experience working with Databricks Unity Catalog, SQL Server Managed Instances, or comparable enterprise data platforms.
- Experience with streaming and batch ingestion frameworks and modern Lakehouse architecture.
- Strong understanding of data quality, data lineage, performance optimization, and enterprise data management principles.
- Familiarity with data governance, data quality, and data management practices aligned with Enterprise Data Management (EDM) standards.
- Experience supporting fraud detection, anomaly detection, financial oversight analytics, or similar analytical environments is preferred.
- Excellent analytical, problem-solving, and communication skills with the ability to collaborate effectively across technical and business teams.
- Due to the nature of the work we support, all candidates selected for this position must be willing to undergo a U.S. Government background investigation.
- Flat files
- JSON
- XML
- Microsoft Excel
- REST APIs
- Graph databases
- Additional structured and unstructured data sources
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