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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** Raas Infotek LLC - **Location:** United States - **Experience:** Expert - **Contract:** Contract - **Skills:** Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Batch Processing, Business Intelligence Development, Big Data, Cloud Computing, Cloud Computing Security, Cloud Database, Cloud Storage, Code Review, Databases, Continuous Integration, Data Architecture, Data Validation, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Mart, Data Transformation, Data Security, Data Systems, Data Warehousing, DevOps, Dimensional Modeling, Distributed Computing Environment, Distributed Systems, Github, Identity and Access Management, Python (Programming Language), Machine Learning, Meta-Data Management, Performance Tuning, Query Optimization, Software Tools, Cloud Services, Data Mesh, DataOps, SQL Stored Procedures, SQL Databases, Data Streaming, Workflow Management Systems, Enterprise Data Management, Data Processing, Freeform SQL, Google Cloud, Cloud Platform System, Feature Engineering, Data Ingestion, Azure Data Factory, Sql Optimization, Delivery Pipeline, Snowflake, Apache Spark, Parallel Computation, Change Data Capture, Gitlab, Git, Event Driven Architecture, Microsoft Fabric, Data Lakes, Pyspark, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Data Lineage, AWS Glue, Star Schema, Apache Kafka, Data Management, Database Replication, Video Streaming, Restful APIs, Terraform, Stream Processing, Azure Synapse Analytics, Software Version Control, Data Pipelines, Docker, Amazon Redshift, Databricks, Programming Languages - **Published:** October 27, 2026 - **Apply:** https://www.dice.com/job-detail/e74caf7a-21ea-4a11-801c-66755bbf1fb7 ## About the Role * Advanced SQL, including complex joins, CTEs, window functions, query optimization, and stored procedures. * Python for data processing, automation, API integration, and ETL/ELT development. * PySpark and Apache Spark for large-scale distributed data processing. 2. Cloud Platforms * Strong hands-on experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud Platform. * Experience with cloud storage, data integration services, compute resources, identity management, and monitoring. * Knowledge of cloud security, scalability, availability, and cost optimization. 3. Data Engineering and Integration * Strong experience building enterprise-grade ETL/ELT pipelines. * Expertise in batch processing, incremental loads, CDC, and streaming data integration. * Experience with Azure Data Factory, Apache Airflow, AWS Glue, or similar orchestration tools. * Familiarity with REST APIs, file-based ingestion, database integration, and event-driven architectures. 4. Data Warehousing and Lakehouse * Experience with Snowflake, Azure Synapse Analytics, Databricks, Microsoft Fabric, Amazon Redshift, or equivalent platforms. * Strong understanding of dimensional modeling, star and snowflake schemas, data marts, and data warehouse architecture. * Experience with Delta Lake, data lakes, lakehouse architecture, partitioning, and schema evolution. 5. Big Data and Streaming Technologies * Experience with Apache Spark and PySpark. * Knowledge of Apache Kafka, Azure Event Hubs, AWS Kinesis, or similar streaming technologies. * Understanding of distributed computing, parallel processing, and large-scale data transformation. 6. DevOps and DataOps * Experience with Git, GitHub, GitLab, Azure DevOps, or equivalent version control and CI/CD tools. * Knowledge of automated testing, deployment pipelines, workflow monitoring, and environment management. * Familiarity with Docker, Kubernetes, Terraform, or other infrastructure automation tools is an advantage., * 10+ years of overall experience in data engineering, data integration, data warehousing, or related technical roles. * Strong hands-on experience designing and implementing complex, enterprise-scale data pipelines. * Proven experience working with large datasets and optimizing data processing workloads. * Experience delivering cloud-based data engineering solutions in production environments. * Strong understanding of data architecture, data modeling, data governance, and data quality principles. * Experience handling production support, performance tuning, and troubleshooting complex data issues. * Demonstrated ability to lead technical initiatives and collaborate with cross-functional teams. * Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience., * Experience with Snowflake features such as Streams, Tasks, Dynamic Tables, and performance tuning. * Experience with Microsoft Fabric, Azure Databricks, or Azure Synapse Analytics. * Knowledge of metadata-driven pipelines, data lineage, and automated data quality frameworks. * Experience implementing data mesh, domain-oriented data platforms, or modern lakehouse architectures. * Familiarity with data catalog and governance tools such as Microsoft Purview or equivalent platforms. * Experience with machine learning data pipelines and feature engineering workflows. * Relevant cloud certifications in Azure, AWS, Google Cloud Platform, or Databricks are a plus. ## Description We are seeking a highly skilled Senior Data Engineer with 10+ years of experience in designing, developing, and maintaining scalable, high-performance data engineering solutions. The ideal candidate will have strong expertise in data architecture, ETL/ELT development, cloud data platforms, data modeling, and big data technologies. This role requires hands-on technical leadership, excellent problem-solving skills, and the ability to collaborate with architects, data scientists, business stakeholders, and cross-functional engineering teams to deliver reliable, secure, and cost-effective data solutions., * Design, develop, and maintain scalable, reliable, and high-performance data pipelines for batch and real-time data processing. * Architect and implement end-to-end ETL/ELT workflows using modern data engineering tools and frameworks. * Build and optimize data solutions on cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform. * Develop complex SQL queries, stored procedures, data transformations, and data models to support analytics and reporting requirements. * Work with cloud data warehouses and lakehouse platforms such as Snowflake, Azure Synapse Analytics, Databricks, and Microsoft Fabric. * Implement data ingestion, transformation, integration, and orchestration processes using tools such as Azure Data Factory, Apache Airflow, or equivalent technologies. * Develop data processing and automation solutions using Python, PySpark, SQL, and other relevant programming languages. * Implement incremental data loading, Change Data Capture (CDC), data replication, and real-time streaming solutions. * Design and maintain data lakes, data warehouses, data marts, and enterprise data platforms. * Establish data quality checks, validation frameworks, data lineage, metadata management, and monitoring mechanisms. * Optimize data pipelines and queries for performance, scalability, reliability, and cost efficiency. * Implement data security, access controls, encryption, governance, and compliance standards. * Collaborate with data architects, business analysts, data scientists, BI developers, and application teams to translate business requirements into technical solutions. * Implement CI/CD pipelines, version control, automated testing, and Infrastructure as Code practices for data engineering workflows. * Troubleshoot complex production issues, identify root causes, and implement permanent solutions. * Mentor junior and mid-level engineers, conduct code reviews, and establish engineering best practices. * Lead technical discussions, contribute to architectural decisions, and drive continuous improvement across data engineering initiatives.