Senior Data Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The company is seeking a Senior Data Engineer to architect, develop, and maintain enterprise-grade data platforms, scalable data pipelines, and distributed data processing systems supporting analytics, business intelligence, and machine learning initiatives. The role involves managing the end-to-end data engineering lifecycle and collaborating with cross-functional teams to deliver secure, reliable, and governed data solutions.

Responsibilities:

• Architect, design, develop, and maintain enterprise-grade data platforms, scalable data pipelines, and distributed data processing systems that support analytics, business intelligence, and machine learning initiatives across multiple business domains
• You will be responsible for the end-to-end data engineering lifecycle, from translating business and analytical requirements into robust data architectures, to developing reliable ETL/ELT pipelines, to deploying cloud-native data solutions and supporting them throughout their operational lifespan
• You will work closely with data scientists, business analysts, software engineers, solution architects, DevOps engineers, and stakeholders in an Agile environment to deliver high-quality, reliable, and governed data platforms that directly support strategic business outcomes

Requirements:

• Bachelor's degree in Computer Science, Information Technology, Data Engineering, Software Engineering, Mathematics, or a closely related technical discipline
• Five or more years of professional experience designing, developing, and supporting production-grade enterprise data engineering solutions, ETL pipelines, and cloud-based data platforms
• Strong, demonstrable understanding of data structures, database design, distributed computing, data modeling, ETL/ELT methodologies, data warehousing concepts, and large-scale data architecture principles
• Advanced working knowledge of Python, SQL, Spark, Scala, Java, and enterprise data engineering frameworks used to build scalable, high-performance data processing solutions
• Hands-on, production-level experience designing and operating batch processing, streaming data pipelines, data lakes, and cloud-native data platforms using technologies such as Databricks, Snowflake, Apache Spark, Kafka, Airflow, or equivalent solutions
• Proven experience working with relational and NoSQL databases including PostgreSQL, SQL Server, Oracle, MySQL, MongoDB, Cassandra, or equivalent database technologies, including schema design, query optimization, indexing strategies, and performance tuning
• Strong SQL skills and meaningful experience designing dimensional models, star schemas, snowflake schemas, data marts, partitioning strategies, indexing, and enterprise-scale data warehouse solutions
• Solid experience with Git-based version control, CI/CD pipelines, DevOps practices, release management, infrastructure automation, and Agile software development methodologies supporting enterprise data engineering initiatives
• Hands-on experience deploying enterprise data platforms and analytics solutions on AWS, Azure, or Google Cloud Platform, including managed storage, compute, networking, security, identity management, and data integration services
• Strong troubleshooting, analytical thinking, debugging, root-cause analysis, communication, and documentation skills, with the ability to investigate complex data processing issues methodically and implement scalable, maintainable engineering solutions
• Experience designing and implementing event-driven architectures, real-time data streaming platforms, Apache Kafka, Apache Flink, Apache NiFi, RabbitMQ, or equivalent enterprise messaging and streaming technologies
• Familiarity with containerization, orchestration, Infrastructure as Code, and cloud-native deployment practices using Docker, Kubernetes, Terraform, Helm, or equivalent enterprise automation technologies
• Exposure to distributed systems concepts including eventual consistency, fault tolerance, distributed transactions, data replication, partitioning strategies, CAP theorem, high availability, and large-scale data processing architectures
• Experience implementing data governance frameworks, master data management (MDM), data lineage, metadata management, data quality automation, security compliance, and DataOps best practices within enterprise cloud and Agile development environments