Note: The job is a remote job and is open to candidates in USA. Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The Senior Data Engineer will architect, design, 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 covers the end-to-end data engineering lifecycle, including data architecture, ETL/ELT pipeline development, cloud-native data solutions, and collaboration with cross-functional teams in an Agile environment.
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
- 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
- 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
Skills
- U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates
- We are unable to sponsor new H-1B visa petitions for this position
- 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
Benefits
- 100% Remote (U.S.)
- Tremendous career growth potential
Company Overview