Hire Data Engineers

We match data engineers based on industry experience and domain knowledge relevant to your sector. Our vetting process includes evaluating past projects in similar industries, understanding compliance requirements like GDPR or HIPAA, and assessing knowledge of industry-specific data formats. We also provide detailed briefings about your business context and data challenges during the onboarding process.

Absolutely. Our data engineers adapt to your current tech stack, whether you're using traditional on-premises solutions or modern cloud platforms. They have experience integrating with existing systems like Oracle, SQL Server, Salesforce, and legacy mainframe systems. They can also help modernize your infrastructure by implementing hybrid cloud solutions and gradual migration strategies.

Our data engineers have extensive experience building real-time streaming architectures using Apache Kafka, Apache Storm, and cloud streaming services like AWS Kinesis and Azure Event Hubs. They've implemented low-latency data processing solutions for applications requiring sub-second response times, including fraud detection, recommendation engines, and IoT sensor data processing. Many have worked on systems processing millions of events per second.

Our data engineers are trained in data security best practices and regulatory compliance frameworks. They implement encryption at rest and in transit, manage access controls using IAM policies, and ensure audit logging for all data operations. They have experience with compliance standards like SOC 2, PCI DSS, and industry-specific regulations. All engineers sign comprehensive NDAs and undergo security background checks.

Most data engineers become productive within 1-2 weeks, depending on project complexity and existing documentation. We accelerate this process through comprehensive onboarding that includes architecture reviews, codebase walkthroughs, and hands-on training sessions. For complex legacy systems, we allow up to 3-4 weeks for full integration. Our engineers are experienced in quickly understanding new environments and contributing meaningfully from day one.

Yes, our data engineers prioritize data governance and comprehensive documentation as part of their standard practice. They implement data cataloging using tools like Apache Atlas, create data lineage documentation, and establish data quality monitoring processes. They also help define data ownership policies, create technical documentation for data pipelines, and implement version control practices for data transformation logic and schema changes.

Our data engineers have strong backgrounds in building ML-ready data pipelines and feature engineering processes. They work with tools like MLflow, Kubeflow, and cloud ML services to create automated model training and deployment pipelines. Many have experience integrating with popular ML frameworks like TensorFlow, PyTorch, and Scikit-learn, and they understand the unique requirements of preparing data for machine learning applications, including feature stores and model versioning.

Our data engineers specialize in designing scalable architectures that handle petabyte-scale datasets efficiently. They use distributed computing frameworks like Apache Spark and implement partitioning strategies, data compression techniques, and query optimization methods. They have experience with auto-scaling cloud resources, implementing caching layers, and optimizing data storage formats like Parquet and Delta Lake for improved performance and cost efficiency.

Our data engineers provide comprehensive ongoing support including monitoring data pipeline health, performance optimization, troubleshooting issues, and implementing updates as business requirements evolve. They set up alerting systems for data quality issues, perform regular maintenance tasks like index optimization, and provide 24/7 support for critical production systems. They also help with capacity planning and technology upgrades to ensure your data infrastructure remains current and efficient.