Role Description We are seeking an experienced Big Data Engineer to design, build, and operate large-scale data processing pipelines and analytics platforms on Hadoop and related big-data ecosystems. In this role you will be responsible for ingesting, transforming, and analyzing massive volumes of structured and unstructured data to support enterprise analytics, machine learning, and reporting workloads. The ideal candidate will combine deep technical expertise across the Hadoop ecosystem with strong software engineering fundamentals and a clear understanding of how to deliver reliable, performant, and cost-effective data platforms in production environments. Key Responsibilities - Design, develop, and operate end-to-end big-data pipelines on Hadoop, ingesting data from a diverse mix of relational, file-based, streaming, and API-driven sources. - Build robust ETL/ELT workflows using Apache Spark, Hive, Pig, and Sqoop, with strong attention to data quality, idempotency, error handling, and recoverability. - Develop high-throughput streaming data pipelines using Kafka, Spark Streaming, or Flink, and integrate them with downstream analytical and operational systems. - Optimize Spark and MapReduce jobs through careful tuning of partitioning, memory, serialization, and skew handling to meet demanding SLAs at minimal cost. - Design and maintain data models and storage layouts on HDFS, Hive, HBase, and modern lakehouse formats (Parquet, ORC, Delta, Iceberg, Hudi) to balance flexibility and performance. - Implement data governance, lineage, and quality controls in collaboration with data governance and security teams. - Build robust monitoring, alerting, and logging strategies for big-data pipelines, including job-level SLAs and proactive failure detection. - Partner with data scientists and analysts to deliver curated, reliable, and well-documented datasets that accelerate their work. - Automate pipeline orchestration using Airflow, Oozie, or similar workflow engines, with clean dependency management and clear ownership boundaries. - Continuously evaluate and adopt new technologies in the big-data and cloud ecosystem (EMR, Databricks, Snowflake, BigQuery) where they offer meaningful improvements. - Lead performance reviews and architecture audits of existing pipelines, proposing concrete refactoring and optimization initiatives. - Document data architectures, schemas, pipeline behaviors, and operational runbooks in a way that makes the platform supportable as the team scales. - Mentor junior engineers and contribute to the team’s engineering standards and best practices. Qualifications - Bachelor’s degree in Computer Science, Engineering, or a related technical discipline. - Five or more years of professional experience designing and operating big-data pipelines on Hadoop. - Strong hands-on expertise with Apache Spark (Scala, Python, or Java) in production environments. - Solid experience with Hive, HDFS, Sqoop, HBase, and the broader Hadoop ecosystem. - Hands-on experience with streaming data platforms such as Kafka, Spark Streaming, or Flink. - Strong SQL skills and experience working with both relational and NoSQL data stores. - Experience with workflow orchestration tools such as Airflow or Oozie. - Solid understanding of distributed systems concepts, including partitioning, replication, and fault tolerance. - Strong scripting skills in Python or Shell. - Excellent troubleshooting, debugging, and documentation skills. Preferred Qualifications - Experience operating Hadoop on cloud platforms such as AWS EMR, Azure HDInsight, or Databricks. - Familiarity with modern lakehouse formats (Delta, Iceberg, Hudi). - Exposure to data governance tooling such as Apache Atlas or Collibra. - Experience with Kubernetes-based data platforms (Spark-on-K8s, Trino). - Hands-on experience with CI/CD and infrastructure-as-code in data engineering workflows. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Role Description We are seeking a skilled Data Integration & Middleware Engineer to implement and manage scalable data pipelines and middleware solutions. The role will focus on orchestrating data workflows using Azure technologies such as Azure Data Factory (ADF), Azure Blob Storage, and Azure SQL Database, ensuring efficient ETL/ELT processing and high-quality data delivery. Qualifications - Strong experience with Azure Data Factory (ADF) for orchestration - Hands-on experience with ETL/ELT development - Experience with Azure Blob Storage / Data Lake - Proficiency in Azure SQL Database / SQL Server - Understanding of data warehousing concepts and data modeling - Knowledge of DevOps, CI/CD pipelines for data workflows - Experience with Azure Databricks or Synapse Analytics (Nice to Have) - Knowledge of data pipeline optimization and monitoring (Nice to Have) - Familiarity with API integrations and data connectors (Nice to Have) - Exposure to data governance and security practices (Nice to Have) - Azure certifications (e.g., AZ-900, DP-203) (Nice to Have) - Experience with real-time data processing frameworks (Event Hub, Service Bus) (Nice to Have) Requirements - Manage end-to-end data integration pipelines using Azure Data Factory (ADF) - Orchestrate and monitor data workflows, ensuring reliability, performance, and error handling - Implement/Manage ETL/ELT processes for data ingestion, transformation, and loading - Manage and optimize data storage solutions using Azure Blob Storage - Maintain structured data models in Azure SQL Database / Azure DB - Ensure data quality, validation, and governance across pipelines - Support integration with on-premise and cloud-based systems - Perform performance tuning and cost optimization across the data platform Benefits - Strong analytical and problem-solving abilities - Clear communication and stakeholder management skills - Ability to work in a collaborative, cross-functional environment - Attention to detail and commitment to data quality

• Lead the design and implementation of data ingestion architectures in Snowflake, ensuring scalability and reliability. • Own the development of end-to-end ELT pipelines integrating multiple data sources. • Design and enforce data quality frameworks, including validation rules, testing strategies, and monitoring. • Apply and drive Medallion Architecture (Bronze, Silver, Gold) best practices across data pipelines. • Build and optimize data pipelines and architectures on AWS (S3, Glue, Lambda, EMR, etc.). • Develop and enhance data validation and ingestion frameworks, including UI components (Streamlit-based) when needed. • Act as a technical leader, guiding best practices in data engineering, governance, and pipeline reliability. • Proactively identify risks, bottlenecks, and data quality issues, and implement mitigation strategies before they impact delivery. • Contribute to data governance initiatives, including data definitions, lineage, and stewardship practices. • Drive documentation and continuous improvement of data platform processes.

• Maintain and evolve corporate data environments • Develop, maintain and optimize ETL (Extract, Transform, Load) processes • Perform database maintenance and tuning to ensure performance, availability and data integrity • Administer and support SAS environments, including tasks related to SAS DBA and SAS DI Studio (SIS) • Support legacy applications and processes, proposing continuous improvements • Monitor, analyze and address failures in data load and integration processes • Handle a significant volume of tickets, meeting established SLAs and ensuring operational continuity • Assist in identifying and resolving incidents related to the data environment • Produce and maintain technical documentation for processes and procedures • Participate in continuous improvement, automation and data architecture modernization initiatives • Support projects related to the implementation and evolution of the Data Lake