Separate, manage, and extract value from data that sits on your systems. Hire data scientists who develop recommendation engines that increase revenue and improve customer satisfaction. Work with professionals who turn data into decisions.
Fill out the form, and leave the rest to our dedicated experts.
Solve real business problems with data scientists who turn your data into real-time insight that moves operations.
Batch data processing workflows
Real-time streaming pipelines
Change data capture (CDC) implementations
Incremental data loading strategies
Data validation frameworks
AWS (S3, Redshift, Glue, EMR)
Google Cloud Platform (BigQuery, Dataflow, Pub/Sub)
Azure (Data Factory, Synapse, Databricks)
Snowflake integration
Multi-cloud architecture design
Star and snowflake schema design
Slowly changing dimensions (SCD)
Kimball and Inmon methodologies
Columnar storage optimization
Data mart development for specific business units
Apache Airflow DAG development
Prefect and Dagster pipelines
Job scheduling and dependency management
Automated data quality checks
Pipeline monitoring and alerting systems
Object storage design patterns
Data lakehouse implementations (Delta Lake, Iceberg)
Partitioning and bucketing strategies
Metadata management and cataloging
Raw-to-curated data layer organization
Apache Kafka and Kafka Streams
Apache Flink and Spark Streaming
Event-driven architectures
Message queue integration (RabbitMQ, AWS SQS)
Low-latency data delivery pipelines
Data engineering projects can be complicated, but not when you understand the systems. We track every metric to ensure your infrastructure aligns with operational demands. Experience the difference professionals bring to your business.
We lock down project boundaries from the start, so feature requests don't turn into scope creep.
Every requirement is documented and validated in advance, so we deliver a solution that solves the problem.
We set realistic deadlines based on actual engineering effort, not wishful thinking. Your project ships with strict deadlines.
Agile sprints and CI/CD pipelines keep our engineers moving fast, delivering consistent progress and measurable results.
We track spending against milestones using CPI and other cost metrics. You stay within budget and get infrastructure that's worth what you paid.
We establish ROI benchmarks beforehand and measure against them throughout the build to deliver value to customers.
Regular financial reports show where every dollar goes. No surprise invoices, no hidden costs.
Our framework identifies potential technical and operational risks early, allowing us to solve problems before they impact delivery.
Every business is different, and so are its data engineering requirements. We offer hiring models that fit your specific situation and budget. Evaluate the scope of your project and choose the model that makes sense.
The time model gives you the flexibility to adapt as you go, ideal for projects with undefined scope or requirements. You pay for the engineering hours and resources dedicated to your project at a pre-agreed hourly rate.
Need someone embedded in your team for the long haul? Our dedicated engineer model gives you a data engineer (or entire team) who works exclusively on your infrastructure as a part of your organization.
If you've got a well-defined project with clear deliverables, our fixed scope model gives you cost certainty from day one. We agree on the scope, timeline, and budget upfront - no surprises, no overruns.

A reliable pipeline architecture is the foundation of a reliable data infrastructure. Our engineers design and build pipelines that move data efficiently from source to destination, whether it's batch processing or real-time streams. We handle ingestion, transformation, and loading so your downstream systems get the data they need.
We handle the entire data integration process, from cleaning and transforming data across disparate sources to loading it into your warehouse or lake. This includes managing schema changes, handling data quality issues, and ensuring your pipelines run reliably. We follow best practices to maintain data integrity across every step.
Building a scalable and performant data warehouse is at the heart of our data engineering services. Our engineers work with modern cloud platforms and tools to create warehouses that meet your analytics and reporting requirements. We optimize for query performance and ensure the infrastructure scales as your data grows.
Choosing the right infrastructure setup and fine-tuning configurations is crucial for keeping costs down and performance high. Our team has deep experience with cloud platforms, distributed systems, and data processing frameworks. We carefully evaluate your workloads and optimize infrastructure to align with your operations and budget.
Nothing surpasses Uncanny when it comes to sourcing reliable data engineers for your business. Don't believe us? Check out our most recent client testimonials to understand what our customers have to say.


Over the years, our engineers have helped hundreds of organizations build data infrastructure that actually works. Check out our latest case studies to get an idea of our approach to data engineering.
Are you planning to connect with our data engineering experts? Check out these frequently asked questions to stay up to date on common queries and prepare for the call.
We offer comprehensive data engineering services, including pipeline development, data warehouse implementation, infrastructure consulting, and workflow orchestration. Do you have other requirements? Call our experts today!
We have years of experience working with organizations across different industries, including finance, e-commerce, healthcare, and SaaS. Our adaptable team can quickly learn and understand the nuances of any industry to deliver effective solutions.
At Uncanny, we strive to provide affordable pricing for our services. We charge customers based on the scope of the project and its complexity, allowing you to choose flexible hiring models (time & materials, dedicated engineer, or fixed scope), and your business's specific requirements.
The project timeline depends on several factors, including the infrastructure complexity, data volume, and integration requirements. The ideal timeline for building data engineering solutions can range from a few weeks to a few months.
Our data engineers are proficient in various programming languages and technologies, including Python, SQL, Scala, and Java. We work with modern tools like Airflow, Spark, Kafka, dbt, and major cloud platforms (AWS, GCP, Azure). We choose the best tools for every project.
