Apache Spark Developer

• Design, develop, and maintain high-performance distributed data processing applications using Apache Spark.
• Build scalable batch and real-time ETL/ELT pipelines processing large volumes of enterprise data.
• Develop Spark applications using PySpark, Scala, or Spark SQL for data transformation, aggregation, and analytics.
• Optimize Spark jobs for memory utilization, partitioning strategies, shuffle performance, and execution efficiency.
• Process structured, semi-structured, and streaming data from enterprise databases, APIs, Kafka, cloud storage, and data lakes.
• Develop reusable Spark libraries, data processing frameworks, and metadata-driven ingestion pipelines.
• Collaborate with cloud engineering teams to deploy Spark workloads on Databricks, EMR, Azure Synapse, or Kubernetes.
• Implement data quality validation, reconciliation, monitoring, and automated error handling across distributed pipelines.
• Integrate Spark applications with enterprise data warehouses, lakehouses, and reporting platforms.
• Participate in architecture reviews, code reviews, technical design discussions, and Agile development activities.
• Troubleshoot production issues involving distributed processing, cluster performance, resource utilization, and data quality.
• Support cloud migration initiatives by modernizing legacy ETL workloads into Spark-based architectures.

• Six or more years of professional software or data engineering experience.
• Four or more years of hands-on Apache Spark development experience in enterprise production environments.
• Strong proficiency in PySpark, Scala, or Spark SQL for distributed data processing.
• Deep understanding of Apache Spark architecture including RDDs, DataFrames, Datasets, Catalyst Optimizer, DAG execution, and Tungsten engine.
• Strong experience with distributed computing concepts including partitioning, shuffling, caching, broadcast joins, and fault tolerance.
• Advanced SQL skills with databases such as SQL Server, Oracle, PostgreSQL, Snowflake, or Teradata.
• Experience working with Hadoop ecosystem technologies including Hive, HDFS, YARN, and Parquet.
• Experience processing streaming data using Spark Structured Streaming, Apache Kafka, or Event Hubs.
• Hands-on experience with cloud platforms including Azure Databricks, AWS EMR, AWS Glue, Azure Synapse Analytics, or Google Dataproc.
• Experience integrating Spark applications with Delta Lake, Apache Iceberg, or Apache Hudi.
• Strong understanding of data warehousing concepts, dimensional modeling, and data lake architecture.
• Experience using Git, CI/CD pipelines, Azure DevOps, GitHub Actions, or Jenkins.
• Strong debugging, troubleshooting, and Spark performance tuning skills.
• Experience working in Agile Scrum development environments.

• Experience building enterprise Lakehouse architectures using Databricks or Delta Lake.
• Familiarity with Apache Airflow, Azure Data Factory, AWS Step Functions, or Control-M for workflow orchestration.
• Experience with machine learning workflows using Spark MLlib, MLflow, or feature engineering pipelines.
• Knowledge of Kubernetes, Docker, and containerized Spark deployments.
• Experience implementing Data Quality frameworks using Great Expectations or Deequ.
• Familiarity with Apache NiFi, Apache Flink, Trino, or Presto.
• Experience working with cloud object storage including Amazon S3, Azure Data Lake Storage (ADLS Gen2), or Google Cloud Storage.
• Knowledge of Infrastructure as Code using Terraform or ARM templates.
• Experience with enterprise monitoring tools including Prometheus, Grafana, Datadog, or OpenTelemetry.
• Cloud certifications in Azure, AWS, Databricks, or Apache Spark-related technologies are highly desirable.

• Enterprise data lakehouse implementation using Databricks and Delta Lake
• Real-time streaming analytics processing billions of daily events
• Large-scale customer analytics and behavioral data platforms
• Financial risk modeling and fraud detection pipelines
• Healthcare clinical and operational analytics solutions
• Cloud migration of legacy Hadoop and ETL workloads
• Machine learning feature engineering and model training pipelines
• Enterprise reporting platforms supporting executive dashboards and self-service analytics
• Distributed data processing infrastructure deployed on Azure and AWS

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.