Help your business to grow fast with our dedicated data engineer.
Hire dedicated data engineers that fit your needs
Dedicated big data engineers
We have dedicated engineers to assist in your project.
Reduce development cost
Reduce up to 60% of the hiring cost and save money on every project.
Flexible models
We help with a flexible engagement model for every business.
Our Remote Staff Solution
With DBM hiring a dedicated data engineer you will be on top of your ideas, in turn development
- Proficient in programming languages such as SQL, Python, and Java.
- Familiar with cloud computing platforms such as Amazon Web Services (AWS) and Microsoft Azure.
- Create visualizations that effectively communicate data insights.
- Can analyze data to identify patterns and trends.
- Well -versed with different PM tools like trello, slack, basecamp etc
Skills
Our Technical Team Experties
HTML
CSS
WEBSOCKET
NODE JS
REACT NATIVE
Rust
What We Do
Services offered by our data engineer
We are highly experienced in big data and give clients an edge over their competitors.
Big data consultation
Our big data engineers conduct in-depth research on products or software and deliver the best benefits.
Development and analysis
Our analyst prepares the technical document or SRS, including use cases, supported devices, commended features, platforms, third-party integrations, and critical project data.
Real-time data processing
Our business analyst can carry out the research depending on the relevant industry. In this manner, businesses can take hefty benefits.
Big data maintenance
With complete research, analysis of the market, and taking useful insights, big data maintenance can be taken out. In this manner, it becomes easier to carry out every project easily.
FAQ
Yes, our Data Engineers excel in building robust data pipelines capable of processing and analyzing diverse unstructured data sources, leveraging technologies like Apache Kafka, Apache Spark, or custom ETL solutions.
Our Data Engineers implement data quality checks, validation rules, and anomaly detection techniques throughout the data pipeline, conducting rigorous testing and monitoring to ensure the accuracy and reliability of data used for analytics and reporting.
Absolutely! Our Data Engineers establish data governance frameworks encompassing data stewardship, metadata management, access controls, and audit trails to ensure compliance with regulatory requirements and industry standards, such as GDPR or HIPAA.
Our Data Engineers employ distributed computing technologies, parallel processing techniques, and optimization strategies to address scalability and performance challenges in big data environments, ensuring efficient processing of large volumes of data with minimal latency.
Yes, our Data Engineers specialize in designing and implementing real-time data streaming architectures using technologies like Apache Kafka, Apache Flink, or AWS Kinesis, enabling the processing and analysis of continuous streams of data in near real-time.
Our Data Engineers adopt flexible data modeling techniques, including schema-on-read, schema-on-write, and NoSQL data models, to accommodate evolving business requirements and diverse data sources, enabling agility and scalability in data processing and analysis.
Absolutely! Our Data Engineers design and implement data lakes or data warehouses using cloud-based or on-premises platforms like AWS S3, Google BigQuery, or Snowflake, providing a centralized repository for storing and analyzing diverse data sets.
Our Data Engineers implement encryption, access controls, data masking, and anonymization techniques to ensure data security and privacy throughout the data lifecycle, adhering to industry best practices and regulatory requirements.
Yes, our Data Engineers collaborate with Data Scientists to design and implement end-to-end machine learning pipelines, encompassing data preprocessing, model training, hyperparameter tuning, and deployment, using state-of-the-art frameworks and technologies.
Our Data Engineers engage in cross-functional collaboration, facilitating communication, knowledge sharing, and alignment of data initiatives with business objectives, to deliver actionable insights and drive data-driven decision-making across the organization.