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Add a Big Data developer who can design resilient batch and streaming pipelines, modernize lakehouses, optimize Spark workloads, and keep data quality, governance, cost and recovery under control.
Hire a Big Data developer to turn fragmented sources into governed, reliable data products with scalable ingestion, transformation, orchestration, observability and ownership.
Design lakehouse, warehouse and data-mesh architectures; modernize batch and streaming pipelines with clear contracts, ownership and service levels.
Connect databases, SaaS applications, event streams, files and APIs through validated schemas, resilient ingestion patterns and secure credentials.
Build maintainable connectors, Spark libraries and processing services when native platform integrations cannot meet scale, latency or governance requirements.
Prepare trusted feature, retrieval and vector-data pipelines with validation, lineage, access controls and cost-aware processing for analytics, ML and generative AI.
Implement Databricks, Snowflake, BigQuery, Redshift and open lakehouse environments with secure networking, storage, compute, CI/CD and disaster recovery.
Test schemas and transformations, monitor freshness and SLAs, trace lineage, tune jobs, manage upgrades and maintain recovery runbooks.
Our Big Data developers bring deep expertise across lakehouse architecture, distributed processing, cloud warehouses, streaming, orchestration, governance and platform operations.
Scalable data products built around explicit contracts, partitioning, lineage and ownership.
Production frameworks selected for workload scale, latency, reliability and team operating capacity.
Build durable domain models, enforce data contracts and publish documented metrics and curated datasets for analytics and AI teams.
Cloud data platforms designed for performance, governance, cost control and business continuity.
Tests, freshness alerts, lineage and cost telemetry so issues are detected before users lose trust.
Optimize jobs, partitioning and compute; test recovery paths and platform upgrades so service levels remain predictable at scale.
Review a representative profile, then request current CVs matched to your data sources, platform, processing patterns, scale, governance, working-hour overlap and support expectations.
DreamzTech replaces a blueprint with a verified client case only when the Big Data contribution, technology, result and permission are documented. Until then, every card below is a solution blueprint, not a completed client engagement.
Environment: Retail and customer analytics
Core Technology: Databricks, Spark, Kafka, object storage, dbt
Solution blueprint, not a client case: batch and event data are standardized into governed bronze, silver and gold layers; identity rules and quality checks create reusable customer data products. Accepted on freshness, reconciliation, lineage, access policy, cost and analyst sign-off.
Environment: Finance and revenue operations
Core Technology: Snowflake, Airflow, dbt, ERP and CRM sources
Solution blueprint, not a client case: source changes are ingested incrementally, transformed through tested models and reconciled before curated marts are released. Accepted on completeness, duplicate handling, role tests, lineage, recovery and finance-owner approval.
Environment: Logistics and operational analytics
Core Technology: Kafka, Flink, BigQuery, schema registry, observability
Solution blueprint, not a client case: events are validated, enriched and aggregated with late-arrival handling and replay controls. Accepted on latency, throughput, schema evolution, alerting, cost limits, recovery and operations sign-off.
Flexible Engagement Models | Fully Signed NDA | Code Security | Easy Exit Policy
A useful matching call begins with what starts the workflow, where data moves, which actions are sensitive and what happens when a connector, AI step or downstream system fails. Share current workflows, sample payloads, execution evidence and access constraints.
Share your data sources, platform and SLAs and we will outline the fastest path to a governed, production-ready Big Data environment.
Our Big Data developers bring deep expertise across lakehouse architecture, distributed processing, cloud warehouses, streaming, orchestration, governance and platform operations.
| Capability | Big Data Expertise |
|---|---|
| Data Architecture | Lakehouse, cloud warehouse, data mesh, medallion layers, dimensional modeling and data contracts |
| Distributed Processing | Apache Spark, PySpark, Scala, Java, Flink, partitioning, caching and workload tuning |
| Streaming & Events | Kafka, Kinesis, Pub/Sub, Event Hubs, schema registries, replay and late-data handling |
| Orchestration & Transformation | Airflow, Dagster, Prefect, dbt, SQL, CI/CD and dependency-aware scheduling |
| Cloud Data Platforms | Databricks, Snowflake, BigQuery, Redshift, Synapse and managed cloud services |
| Storage & Table Formats | S3, ADLS, GCS, Delta Lake, Apache Iceberg, Hudi, Parquet and lifecycle controls |
| Governance & Security | Catalogs, lineage, IAM, encryption, masking, row/column policies, retention and audit |
| Quality & Observability | Schema tests, reconciliation, freshness SLAs, metrics, alerts, lineage and incident runbooks |
| Performance & FinOps | Cluster sizing, partition strategy, query optimization, storage design and cost telemetry |
Hire a dedicated Big Data developer through a focused, transparent process. Share your platform and backlog, review matched profiles and start with clear access, ownership and acceptance criteria.
Tell us your sources, warehouse or lakehouse, processing patterns, scale, SLAs and delivery backlog. We will match the right Big Data talent to your environment.
Review pre-vetted profiles, interview the strongest matches and select the developer with the right platform, language and operating experience.
Confirm a realistic start date once availability, interviews, contracting, environments, security review, sample data and stakeholder access are known.
Hire Big Data developers who deliver governed, reliable data products across industries so teams can make decisions with confidence.
Strong Big Data delivery combines platform architecture, distributed systems, cloud engineering, data modeling, quality, security and operational ownership. DreamzTech can connect your developer to AI, cloud, QA, security and product specialists when the backlog crosses role boundaries.
Share your data sources, platform, volume, latency, governance requirements and delivery gap. We will respond with the likely developer profile, readiness questions and a practical first scope.
Got questions about hiring a Big Data developer? Explore the FAQs below.
A Big Data developer designs, builds and supports scalable batch and streaming data systems. The role can include ingestion, Spark processing, Kafka streams, lakehouse and warehouse modeling, orchestration, cloud infrastructure, data quality, lineage, security, performance tuning and documentation.
Check production experience with distributed processing, SQL and data modeling; depth in Python, Scala or Java; cloud-platform skills; orchestration, streaming, quality, governance, observability and cost judgment; testing habits; and communication. Ask the developer to explain a failed pipeline, its root cause and how recurrence was prevented.
Use a Big Data developer when the work involves production ingestion, distributed computation, streaming, platform architecture, performance, reliability or governance. A data analyst typically focuses on querying, dashboards and business interpretation. Mature teams often pair both roles.
DreamzTech can match developers for Databricks, Snowflake, BigQuery, Redshift and Synapse, plus Apache Spark, Kafka, Flink, Airflow, dbt, Delta Lake, Iceberg and major AWS, Azure and Google Cloud data services. The final profile depends on your existing stack and target architecture.
Use least-privilege IAM, encryption, masking, governed catalogs, schema and quality tests, lineage, idempotent pipelines, bounded retries, observability, backups and tested recovery. Separate development and production environments and review sensitive-data retention and residency requirements.
Cost depends on seniority, platform complexity, sources, batch or streaming needs, scale, latency, governance, support coverage and working-hour overlap. DreamzTech publishes a starting rate of $20 per hour or $3,200 for a 160-hour monthly allocation for this role; fixed projects require discovery.
Profile matching can begin after the platform, sources, scale, security boundaries and engagement model are clear. The actual start date depends on availability, interviews, contracting, environments and access, so DreamzTech confirms a realistic date rather than promising automatic onboarding.