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Add data engineers who can take ownership of cloud platforms, ETL and ELT pipelines, warehouses, lakehouses, streaming systems, data quality and production support across AWS, Azure and Google Cloud. See our broader data engineering services when the work is scoped as a managed engagement rather than staff augmentation.
Hire data engineers for a defined delivery gap or ongoing platform ownership — from architecture and ingestion to trusted models, observability, governance and cost control. Scope each role around the work it must own, not a generic list of tools. See our full data engineering services when the engagement is broader than staff augmentation.
Assess sources, workloads, ownership, latency, security and cost, then shape a warehouse, lake, lakehouse or hybrid platform your team can operate.
Build tested batch pipelines with explicit schema handling, retries, backfills, reconciliation, orchestration and documented business transformations.
Implement governed workloads across Snowflake, Databricks, BigQuery, Redshift, Synapse or Microsoft Fabric according to the client environment.
Engineer event-driven pipelines with ordering, deduplication, replay, late-data handling, dead-letter paths and measurable latency objectives.
Add contracts, tests, lineage, freshness checks, access controls, masking, auditability and ownership so failures become visible and actionable.
Introduce CI/CD, infrastructure as code, environment promotion, monitoring, query or job tuning, runbooks and spend accountability.
Our data engineers bring hands-on experience across architecture, orchestration, warehousing, streaming, governance and the AI-ready data layers our AI software development team builds on top of — the layers a production data platform actually depends on.
Warehouse, lake, lakehouse or hybrid platform design matched to source systems, ownership and cost, not a default template.
Tested batch pipelines with explicit schema handling, retries, backfills, reconciliation and documented transformations.
Governed delivery across Snowflake, Databricks, BigQuery, Redshift, Synapse or Microsoft Fabric, matched to the client's environment.
Event-driven systems with ordering, deduplication, replay, late-data handling and measurable latency objectives.
Contracts, tests, lineage, freshness checks, access controls and masking implemented as part of delivery, not a final checklist.
CI/CD, infrastructure as code, environment promotion, monitoring, job tuning and runbooks so failures become visible and actionable.
Review a representative role profile, then ask us for two or three current CVs matched to your cloud, sources, platform, latency, governance, support model and required working-hour overlap.
These projects are already published on DreamzTech's site. Each card links to the full case study for complete metrics and context.
Industry: Transportation & Logistics
Core Technology: Snowflake, Power BI, SQL Server, ETL
Legacy SSRS reporting produced fragmented KPIs and slow report generation. DreamzTech migrated the platform to Snowflake, delivered Power BI dashboards with row-level security and automated ETL, and reduced report load times from 30 seconds to under 10 seconds while maintaining a 99% weekly data-health check pass rate.
Industry: Consumer Beverage
Core Technology: Unified analytics platform, historical decomposition modeling
The client needed consistent attribution across volume, net revenue, market share and ROI. DreamzTech built a unified analytics platform with automated reporting, cutting manual reporting workflows by roughly 40% and reducing report generation time by approximately 60%.
Industry: Real Estate Data Aggregation
Core Technology: Multi-source ingestion, AVM/CMA, searchable data platform
DreamzTech built pipelines for deeds, liens, mortgages, tax assessments and permits covering over 90% of U.S. counties, plus an automated valuation engine. The platform generated 100,000+ property reports in its first six months, reached 12,000+ monthly active users and holds a 74% monthly retention rate, with the search application built alongside our custom software development team.
Simple & Transparent Pricing | Fully Signed NDA | Code Security | Easy Exit Policy
A useful matching call begins with the systems, workload and operating responsibility — not a list of buzzwords. Share what is failing, what must be built and how your team will judge whether the engineer is succeeding.
Share your data engineering requirements and we will design the fastest path to a governed, production-ready platform using proven architectures and our delivery team.
A capability map, not a promise that one engineer knows every product — the approved profile is matched to your selected cloud, platform, language, workload and production responsibility. Visualization needs are covered alongside our BI software development team, and if your workload centers on one specific platform, see our hire Databricks developers or hire Snowflake developers pages.
| Core Data Engineering Tools | Apache SparkPySparkApache AirflowDagsterdbtApache KafkaApache FlinkApache Beam |
| Cloud Platforms | AWSMicrosoft AzureGoogle CloudSnowflakeDatabricksAmazon RedshiftGoogle BigQueryAzure SynapseMicrosoft Fabric |
| Data Integration | FivetranAirbyteStitchKafka ConnectDebeziumREST/GraphQL APIsChange Data Capture |
| ETL Tools | AWS GlueAzure Data FactoryData Factory in Microsoft FabricGoogle Cloud DataflowInformaticaMatillionTalenddbt |
| Programming Languages | PythonSQLScalaJavaBash |
| ML Frameworks | MLflowscikit-learnXGBoostPyTorchTensorFlow |
| AI Tools | RAG Ingestion PipelinesVector & Relational Serving LayersLangChainLlamaIndexApproved LLM Provider Integrations |
| Streaming | Apache KafkaAmazon KinesisAzure Event HubsGoogle Pub/SubSpark Structured StreamingApache Flink |
| Visualization | Power BITableauLookerSigmaApache Superset |
| DevOps | TerraformDockerKubernetesGitHub ActionsGitLab CIAzure DevOps |
| Version Control | GitHubGitLabBitbucketAzure Repos |
Hire dedicated data engineers for your project with a clear, efficient hiring process. Build your data-driven team faster and start onboarding matched engineers.
Tell us your workload, cloud, seniority and overlap needs so we can start matching the right data-engineering profiles.
Review matched data engineer profiles and interview the ones that fit your workload and working hours.
Confirm scope, contracting, security review and access needs, then start onboarding on a realistic, confirmed date.
Hire data engineer(s) who deliver scalable, high-performance data solutions across the industries we already serve.
A capable data engineer does more than move records from one system to another. DreamzTech can combine data, cloud, software, QA and delivery skills so the person you add understands the application, report, model or operational workflow that depends on that pipeline.
Share the pipeline, migration, platform, quality, streaming or AI-data workload you need to move forward. We will respond with the likely role or team shape, relevant profiles, delivery questions and a practical next step.
Got questions about hiring a data engineer? Explore direct answers below on role scope, skills, team shape, cloud fit and cost.
A data engineer designs, builds and operates the systems that collect, transform, store and serve data for analytics, applications and AI. In production, the role also covers testing, orchestration, monitoring, security, incident handling, cost and documentation — not only writing ETL code. Related reporting and modeling work is covered by our data analytics services team.
A data engineer should be strong in SQL, data modeling, one production language such as Python, pipeline design, cloud storage and compute, orchestration, testing, version control and troubleshooting. The right profile may also need Spark, Kafka, dbt, Airflow, Snowflake, Databricks, Fabric, BigQuery, Redshift or governance experience, depending on the workload.
Hire one engineer when the architecture is reasonably clear, ownership boundaries are stable and your existing team can provide product, platform and review support. Use a dedicated pod when the work spans architecture, multiple pipelines, migration, data quality, QA, DevOps and delivery coordination, or when several workstreams must move in parallel.
Yes, provided the matched engineer has relevant experience with your selected cloud and platform. DreamzTech can staff roles across AWS, Azure and Google Cloud and work within existing identity, network, repository and deployment standards; the profile should be validated against the exact services and production responsibilities before onboarding.
Cost depends on seniority, cloud and platform depth, streaming or governance specialization, working-hour overlap, delivery oversight and whether you need one engineer or a managed team. DreamzTech’s published starting rate is $20 per hour or $3,200 for a 160-hour monthly allocation once sales confirms the role; fixed projects require scope and dependency review.
Profile matching can begin after the workload, platform, seniority, overlap and ownership expectations are clear. The actual start date depends on engineer availability, interviews, contracting, security review and environment access, so DreamzTech provides a confirmed onboarding date rather than an automatic 48-hour promise.
DreamzTech can work under NDA and contractual IP terms, while delivery access should follow least privilege and the client’s security policies. Depending on the environment, controls may include tests and reconciliation, approved repositories, separate roles, managed secrets, masking or tokenization, audit logs, monitored access and documented offboarding; the signed agreement governs final obligations.