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Add an ETL developer who can connect the sources you actually run, turn business rules into tested transformations and keep data moving after the first successful load — not just build another fragile job your team is afraid to change.
Hire an ETL developer when a source keeps changing, backfills take too long, totals do not reconcile or nobody wants to touch the nightly jobs. The work can begin with a focused repair, a defined migration or ongoing capacity inside your data team — distinct from our broader data engineering services.
Build batch, micro-batch or event-driven flows with explicit source contracts, transformation ownership, dependencies, retry behavior and operational acceptance criteria.
Ingest data from databases, files, APIs, SaaS applications, ERP/CRM platforms, queues and CDC feeds with schema and credential changes handled deliberately.
Turn business rules into versioned SQL, Python, Spark or dbt transformations with profiling, validation, deduplication, exception handling and reconciliation.
Design incremental loads, partitioning, merge strategies, indexing and workload patterns for Snowflake, Databricks, Redshift, BigQuery, Synapse and other approved targets.
Inventory dependencies, translate mappings, run old and new flows in parallel, reconcile outputs and retire Informatica, SSIS, Talend or custom jobs in controlled waves.
Add CI/CD, automated tests, freshness and volume checks, lineage, alerts, runbooks, incident ownership, cost reviews and a disciplined change process.
Our ETL developers bring hands-on experience across extraction, transformation, orchestration and data quality — connected to our broader hire data engineers capacity when the work extends beyond pipeline implementation.
Extraction patterns matched to source constraints and freshness needs, from nightly batch to change-data-capture streams.
Connectors for databases, files, APIs, SaaS platforms and queues, with schema and credential changes handled deliberately.
Versioned transformation logic with profiling, validation, deduplication and exception handling instead of untracked scripts.
Source-to-target reconciliation, schema and contract tests, and controlled reprocessing so a completed job means a correct one.
Dependency-aware scheduling, retries and alerting across Airflow, cloud-native orchestrators and workflow tools.
Incremental loads, partitioning and merge strategies tuned for the target platform's actual workload pattern.
Review a representative role profile, then ask us for two or three current CVs matched to your sources, target platform, transformation approach, orchestration, latency, data volume, security constraints and working-hour overlap.
These three 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, Automated ETL
Legacy SSRS reporting was slow and fragmented. DreamzTech migrated the reporting platform to Snowflake with automated ETL and delivered Power BI dashboards, reducing report load times to under 10 seconds, cutting report-generation time by roughly 60% and maintaining a 99% weekly data-health check pass rate across 150+ active users.
Industry: B2B Technology / Enterprise Sales
Core Technology: AWS Data Lake, Custom ETL, Entity Resolution
The client had 2.3 million customer records split across Salesforce, HubSpot and a legacy Access database. DreamzTech built an ETL pipeline that deduplicated 340,000 overlapping records at 99.2% accuracy and migrated 2.3M records with zero business disruption.
Industry: Real Estate Data Aggregation
Core Technology: Multi-Source Public-Record Ingestion, Search, AVM/CMA
DreamzTech built ingestion pipelines for deeds, liens, mortgages, tax assessments and permits from thousands of sources, covering more than 90% of U.S. counties. The platform generated 100,000+ reports in six months, reached 12,000+ monthly active users and holds a 74% monthly retention rate.
Simple & Transparent Pricing | Fully Signed NDA | Code Security | Easy Exit Policy
A useful matching call starts with the source, target, failure mode and business deadline. Tell us what must arrive, how fresh it needs to be and how you will know the numbers are correct.
Share your pipeline requirements and we will design the fastest path to a reliable, operable data flow using proven patterns and our delivery team.
A capability map, not a promise that one developer knows every product — the approved profile is matched to your source and target systems, deployment model, volume and latency. If the work is really an architecture or reporting question, see our hire data architect or hire data analysts pages, and for platform-specific engineering see our hire Snowflake developers or hire Databricks developers pages.
| Cloud Platforms | AWSMicrosoft AzureGoogle Cloud |
| ETL & Integration Platforms | Informatica PowerCenter/IICSMicrosoft SSISTalendAWS GlueAzure Data FactoryGoogle Cloud DataflowMatillionFivetranAirbyte |
| Transformation & Modeling | dbtSQL ModelsSpark/PySparkStored ProceduresData VaultMedallion Patterns |
| Orchestration & Workflow | Apache AirflowCloud ComposerAzure Data FactoryAWS Step FunctionsDatabricks Workflows |
| Programming & Query Languages | SQLPythonPySparkScalaJavaBash/PowerShell |
| Sources & Connectors | PostgreSQLSQL ServerOracleMySQLMongoDBERP/CRM/SaaS APIsSFTP/Files |
| Warehouses & Lakehouses | SnowflakeDatabricksAmazon RedshiftGoogle BigQueryAzure SynapseMicrosoft FabricS3ADLS |
| Streaming & CDC | Apache KafkaConfluentDebeziumAWS KinesisAzure Event HubsGoogle Pub/Sub |
| Data Quality & Observability | dbt TestsGreat ExpectationsSodaMonte CarloReconciliation Frameworks |
| DevOps & Infrastructure | GitHub/GitLab/Azure ReposCI/CDDockerTerraformKubernetes |
| BI & Downstream Serving | Power BITableauLookerSemantic ModelsAPIsReverse ETL |
Hire a dedicated ETL developer for your project with a clear, efficient hiring process. Move from a fragile job to an operable pipeline faster.
Tell us your sources, target platform, current ETL stack and overlap needs so we can start matching the right ETL-developer profiles.
Review matched ETL developer profiles and interview the ones that fit your pipeline environment and working hours.
Confirm scope, contracting, environment access and data-owner dependencies, then start onboarding on a realistic, confirmed date.
Hire ETL developers who deliver scalable, high-performance data pipelines across the industries we already serve.
A reliable ETL pipeline is not defined by the tool used to draw it. It is defined by whether the data arrives on time, can be reconciled, survives schema change, can be reprocessed safely and has an owner when something fails. DreamzTech can connect the developer to architects, analysts, cloud engineers, QA and product teams when the work crosses role boundaries.
Share your sources, target platform, current failures, data volume and deadline. We will respond with the likely ETL profile, readiness questions and a practical first scope.
Got questions about hiring an ETL developer? Explore direct answers below on role scope, readiness, testing and cost.
An ETL developer builds and operates the processes that extract data from source systems, transform it into agreed structures and load it into a warehouse, lakehouse, application or downstream service. The work often includes connectors, mapping, validation, orchestration, incremental loads, performance tuning, deployment, monitoring, documentation and production support.
ETL transforms data before loading it into the target; ELT loads raw or lightly processed data first and performs much of the transformation inside a cloud warehouse or lakehouse. Data engineering is the broader discipline covering architecture, storage, pipelines, quality, governance and operations — see our hire data engineers page for that wider role. An ETL developer usually focuses on implementation and reliability within that wider system.
Hire a dedicated ETL developer when sources, mappings and priorities will continue to change, or when the person must own an ongoing pipeline backlog and production incidents. Use a fixed project when the source inventory, target design, deliverables, acceptance criteria and cutover plan are stable enough to estimate responsibly.
Prepare the source and target systems, sample schemas, expected volumes and latency, transformation rules, known quality issues, security constraints, current jobs and failure history. Name the data owners who can explain business rules, approve access, reconcile totals and sign off on cutover; technical access without accountable owners slows the work.
Testing should cover schema and contract changes, transformation logic, nulls and duplicates, source-to-target reconciliation, incremental and late-arriving data, retries, idempotency or controlled reprocessing, performance, permissions and failure alerts. Production acceptance should also include monitoring, runbooks, ownership and a tested recovery path.
Cost depends on seniority, source and target platforms, data volume and latency, transformation complexity, legacy-tool expertise, security requirements, working-hour overlap and support coverage. DreamzTech’s published starting rate is $20 per hour or $3,200 for a 160-hour monthly allocation once sales confirms the selected role; fixed projects require discovery.
Profile matching can begin after the sources, targets, tools, responsibilities, working-hour overlap and engagement model are clear. The actual start date depends on developer availability, interviews, contracting and environment access, so DreamzTech confirms a realistic date rather than promising an automatic 48-hour start.