Companies in this catalog do not charge applicants. Never send money to apply.
Data Engineer
Role Overview
The Data Engineer is responsible for designing, building, and maintaining scalable, cloud�native data pipelines and data infrastructure that support analytics, reporting, business intelligence, and real-time data processing. This role ensures that data is accessible, reliable, secure, and optimized for performance across enterprise platforms. The Data Engineer collaborates with business stakeholders, analysts, data scientists, and application teams to deliver high-quality data solutions using modern cloud, big data, and API-driven technologies.
Key Responsibilities
Design, develop, and maintain scalable ETL/ELT pipelines for batch and real-time data processing.
Build and optimize data models, Delta Tables, and Lakehouse architectures to support analytics and reporting.
Develop and integrate RESTful APIs and data services to facilitate seamless data exchange across enterprise systems.
Implement real-time and high-frequency data ingestion frameworks using streaming technologies and event-driven architectures.
Design and manage cloud-native data solutions leveraging Azure services including
Azure Data Factory, Azure Databricks, ADLS, Event Hubs, and Synapse Analytics.
Develop and optimize Databricks Spark applications for large-scale data transformation and processing.
Ensure data quality, governance, security, and compliance across data platforms.
Collaborate with data scientists, analysts, application teams, and business stakeholders to deliver scalable data solutions.
Troubleshoot, monitor, and optimize pipeline performance and data platform reliability.
Support DataOps and CI/CD practices for data pipeline deployment and automation.
Required Skills & Qualifications
Strong proficiency in SQL and relational databases such as Oracle, SQL Server, and
MySQL.
Strong programming skills in Python, PySpark, PL/SQL, Java, or Scala.
Hands-on experience with Azure Cloud technologies: o Azure Data Factory (ADF)
o Azure Databricks
o Azure Data Lake Storage (ADLS) o Azure Synapse Analytics o Azure Event Hubs
o Azure Functions
o Azure API Management
o Azure DevOps
Experience with Databricks Lakehouse architecture, Delta Lake, and Delta Tables.
Expertise in API development, API integration, RESTful services, and microservices architecture.
Experience processing high-volume and high-frequency data with low-latency requirements.
Strong knowledge of real-time data ingestion and streaming technologies such as
Kafka, Azure Event Hubs, or Kinesis.
Experience with Spark, Hadoop, and distributed data processing frameworks.
Hands-on experience with OpenShift, Kubernetes, Docker, and containerized deployments.
Experience with workflow orchestration tools such as Apache Airflow and Azure
Data Factory.
Understanding of data governance, data security, and compliance best practices.
Preferred Qualifications
Experience with Delta Live Tables (DLT), Auto Loader, and Change Data Capture (CDC).
Knowledge of DataOps, CI/CD, and Infrastructure as Code (IaC).
Familiarity with event-driven architectures and real-time analytics platforms.
Azure Data Engineer (DP-203) and Databricks certifications