Experience in data integration projects using Big Data technologies, preferably in human resources analytics
Proven ability to manage engineering teams
Hands-on experience with AWS Data Warehousing and database platforms (Databricks preferred), including EMR, S3, AWS Glue, Lambda, Apache Airflow, and Infrastructure as Code tools (e.g., Terraform, CloudFormation)
Proficiency in Spark engineering with PySpark or Scala for scalable data pipelines
Experience building streaming applications using Kafka
Strong background in CI/CD using Jenkins, Git, Artifactory, YAML, and Maven for cloud deployments
Knowledge of Big Data querying tools such as Athena, RDS, and Databricks
Experience integrating data from various file-storage formats (Parquet, ORC, Avro) and table formats (Delta Lake, Iceberg)
Familiarity with Java open-source tools, API standards, JUnit, Spring Boot, and Swagger
Experience using AI-assisted developer productivity tools (e.g., GitHub Copilot)
Technical expertise in building scalable, high-performance distributed systems
Knowledge of Data Warehousing, Data Modeling, Data Lake, and Data Security concepts
Strong problem-solving, troubleshooting, and analytical skills
Excellent communication, presentation, and interpersonal skills
Experience with Oracle/SQL and NoSQL data stores such as DynamoDB
Ability to quickly learn new technologies in a dynamic environment
Preferred Qualifications, Capabilities, and Skills
Experience leading data integration projects in large, complex organizations
Advanced knowledge of cloud-based data solutions
Familiarity with emerging technologies in data engineering and analytics
Demonstrated ability to mentor and develop engineering talent
Experience working in agile development environments
Strong track record of delivering innovative solutions
Commitment to fostering an inclusive and collaborative team culture