Key skills
PythonSQLETL processesGolangKubernetesApache SparkCloud platformsData modelingData transformationClean coding practicesCI/CD pipelinesMonitoring toolsInfrastructure-as-codeData quality toolsContainer orchestrationProblem-solving skillsAIMachine LearningETLData LakeSnowflakeRedshiftDatabricksSparkAirflowAWSAzureGCPTerraformDockerAnsibleHelmPostgreSQLSQL ServerPrometheusGrafanaELK StackCI/CD
About this role
Machinify is a leading healthcare intelligence company focused on delivering value and efficiency to health plan clients. They are seeking a skilled Staff Data Platform Engineer to build scalable data infrastructure and collaborate with cross-functional teams to enhance their AI-powered platform.
Responsibilities:
- Design and build scalable and high-performance data software solutions using Golang and Python
- Build and deploy Kubernetes-based systems to manage containerized applications in cloud-native environments
- Collaborate with cross-functional teams to understand and address customer needs, ensuring our systems evolve to meet future requirements
- Optimize performance, reliability, and security of our backend infrastructure
- Work on data modeling and transformation to support machine learning workflows and AI-powered applications
Requirements:
- 10+ Years in software development with Python and SQL
- Strong understanding of ETL processes, big data pipelines, and relational databases (PostgreSQL, SQL Server, etc.)
- Experience with orchestration tools (e.g., Airflow, Prefect, Dagster)
- Knowledge of cloud platforms (AWS, Azure, GCP) and their ecosystem tools
- Strong problem-solving skills and clean coding practices
- Experience with Apache Spark or similar technologies
- Experience with agentic coding workflows
- Experience with data modeling, transformation, and integration
- Experience with data quality, lineage, and modeling tools
- Experience in software development with Golang
- Experience with managed data lake systems (e.g., Databricks, Snowflake, AWS Redshift)
- Familiarity with Kubernetes and container orchestration (Docker, Helm, etc.)
- Familiarity with infrastructure-as-code systems (e.g., Terraform, Ansible)
- Knowledge of monitoring and logging tools for distributed systems (Prometheus, Grafana, ELK stack)
- Experience with CI/CD pipelines and automated testing
- Strong understanding of data modeling, ETL workflows, and data transformation techniques