Big Data Staff Augmentation Services
Scale Your Data Engineering Capacity Without the Overhead of a Full-Time Hire
Rapid Onboarding in <48 Hours
No Hidden Charges
IP Rights & NDA Protection
Data Security & Confidentiality Guaranteed
No Long-Term Lock-In
Flexible Hiring Model
Rapid Onboarding in <48 Hours
No Hidden Charges
IP Rights & NDA Protection
Data Security & Confidentiality Guaranteed
No Long-Term Lock-In
Flexible Hiring Model
500+
Developers
Placed Across RPA, CRM, mobile, QA, and data engineering. Certified, verified, and ready to join your team.
48-Hour
Onboarding
From requirement to a productive developer in under 48 hours. No lengthy recruitment cycles, no wasted sprints.
100%
Transparent Pricing
You pay for actual hours or an agreed monthly rate. No hidden fees, no scale-down penalties. Full cost structure shared before you sign.
Zero
Lock-In Contracts
Scale up or down at any point. No penalties, no minimum commitments beyond the first month.
Hire Big Data Experts - Who
Knows it All
Big data is not a single discipline. It is twenty-plus technologies, each with its own architecture, operational model, and failure behaviour. Kafka specialists spend years on partition strategy and broker tuning alone. NiFi engineers go deep on processor internals and backpressure mechanics. Iceberg, Hudi, Spark, ClickHouse, Airflow, each one is its own depth.
No internal data team covers all of it well. The ones that try end up with broad familiarity across most tools and genuine depth in very few. What enterprises need is not ownership of every discipline; it is reliable access to depth when a project demands it.
Ksolves places specialists who know the tool your project runs on, at the level the project requires. With 12+ years across the full big data stack and open-source contributions to NiFi, Kafka, and Cassandra, the bench runs deep where it matters. Not generalists filling a seat, the right depth, matched to the right workload, from day one.
Find the Right Big Data Specialist
for Your Stack
Our big data staff augmentation services cover every specialist your project demands, across every tool your stack runs on.
Builds and maintains the pipelines your data operations depend on, including ingestion, routing, transformation, and delivery across real-time and batch environments. Whether you need to hire a NiFi developer, a Kafka engineer, a Flink specialist, or someone who works across multiple tools in your stack, Ksolves places data pipeline engineers matched to exactly what your project runs on, not the closest available generalist.
Connects disparate systems, on-premise databases, cloud platforms, SaaS tools, and third-party APIs into a unified, reliable data flow. Our big data staffing solutions cover Informatica, Talend, NiFi, StreamSets, and MuleSoft, making them particularly valuable during migrations from legacy ETL tools to modern integration platforms where the cost of a wrong placement shows up immediately in project delays.
Handles large-scale batch and streaming compute jobs across Spark, Databricks, and Hadoop. Covers PySpark and Scala development, Delta Lake and Iceberg integration, and performance tuning for jobs that are running slow, failing under load, or consuming more compute than they should. Our big data outsourcing model means you bring in this depth for the sprint that needs it, not a permanent hire for a problem that has a defined endpoint.
Designs and optimises how data is stored, accessed, and retained at scale. Works across Cassandra, Iceberg, Hudi, Delta Lake, ClickHouse, and HBase, matching storage architecture to query patterns, retention requirements, and cost constraints. Most storage problems are not tool problems. They are design problems. Our big data staffing solutions put a specialist on them before they compound.
Designs and manages the workflow layer that keeps pipelines running reliably. Covers Airflow DAG development, dbt model structuring, dependency management, and failure handling. Engaged when pipelines are technically sound but operationally fragile, jobs failing silently, schedules drifting, or no visibility into what ran and what did not. A focused hire through big data staff augmentation resolves this faster than redistributing the problem across an already stretched internal team.
Builds and optimises data infrastructure on AWS, Azure, and GCP, using native services including Glue, EMR, MSK, Data Factory, Synapse, Dataflow, and BigQuery. Relevant for teams migrating on-premises workloads to the cloud, running hybrid architectures, or consolidating across multiple cloud environments. Ksolves' big data staffing model places cloud data engineers with platform-specific depth, not broad cloud familiarity applied to a data problem.
Design the infrastructure layer before the build begins, lakehouse architecture, integration patterns across the data stack, cloud migration planning, and governance frameworks. Engage a big data architect when the project is large enough that getting the design wrong costs more to fix than it did to build. This is where big data outsourcing pays for itself earliest, one architect engagement that prevents a six-month rework.
Turns processed data into usable reporting and analytical layers using Trino, Druid, Power BI, and Tableau. Works downstream of the pipeline, structuring data models, building dashboards, and ensuring the right data reaches the right decision-maker in the right format. Often, the last role considered in a data build is the first one stakeholders notice when it is missing.
Builds and maintains the pipelines your data operations depend on, including ingestion, routing, transformation, and delivery across real-time and batch environments. Whether you need to hire a NiFi developer, a Kafka engineer, a Flink specialist, or someone who works across multiple tools in your stack, Ksolves places data pipeline engineers matched to exactly what your project runs on, not the closest available generalist.
Connects disparate systems, on-premise databases, cloud platforms, SaaS tools, and third-party APIs into a unified, reliable data flow. Our big data staffing solutions cover Informatica, Talend, NiFi, StreamSets, and MuleSoft, making them particularly valuable during migrations from legacy ETL tools to modern integration platforms where the cost of a wrong placement shows up immediately in project delays.
Handles large-scale batch and streaming compute jobs across Spark, Databricks, and Hadoop. Covers PySpark and Scala development, Delta Lake and Iceberg integration, and performance tuning for jobs that are running slow, failing under load, or consuming more compute than they should. Our big data outsourcing model means you bring in this depth for the sprint that needs it, not a permanent hire for a problem that has a defined endpoint.
Designs and optimises how data is stored, accessed, and retained at scale. Works across Cassandra, Iceberg, Hudi, Delta Lake, ClickHouse, and HBase, matching storage architecture to query patterns, retention requirements, and cost constraints. Most storage problems are not tool problems. They are design problems. Our big data staffing solutions put a specialist on them before they compound.
Designs and manages the workflow layer that keeps pipelines running reliably. Covers Airflow DAG development, dbt model structuring, dependency management, and failure handling. Engaged when pipelines are technically sound but operationally fragile, jobs failing silently, schedules drifting, or no visibility into what ran and what did not. A focused hire through big data staff augmentation resolves this faster than redistributing the problem across an already stretched internal team.
Builds and optimises data infrastructure on AWS, Azure, and GCP, using native services including Glue, EMR, MSK, Data Factory, Synapse, Dataflow, and BigQuery. Relevant for teams migrating on-premises workloads to the cloud, running hybrid architectures, or consolidating across multiple cloud environments. Ksolves' big data staffing model places cloud data engineers with platform-specific depth, not broad cloud familiarity applied to a data problem.
Design the infrastructure layer before the build begins, lakehouse architecture, integration patterns across the data stack, cloud migration planning, and governance frameworks. Engage a big data architect when the project is large enough that getting the design wrong costs more to fix than it did to build. This is where big data outsourcing pays for itself earliest, one architect engagement that prevents a six-month rework.
Turns processed data into usable reporting and analytical layers using Trino, Druid, Power BI, and Tableau. Works downstream of the pipeline, structuring data models, building dashboards, and ensuring the right data reaches the right decision-maker in the right format. Often, the last role considered in a data build is the first one stakeholders notice when it is missing.
The Right Big Data Engineer for Your
Stack Is Already in Our Bench
Stop spending months finding the talent your organization needs right now.
Ksolves' big data staffing
solutions match you with the right
specialist, by tool, by depth, by timeline.
Why Choose Ksolves as Your Big Data
Staff
Augmentation Company
Our big data staffing solutions bring you deep stack expertise, open-source contributions, and engineers
who
have solved the exact problem your project is facing, not a
generalist IT shop filling seats.
90%
Client Retention
Rate
750+
Projects Successfully
Delivered
NSE & BSE
Publicly Listed
Company
600+
Workforce and still growing
350+
Certifications
stats background
200+
Happy Clients
24x7
Support Across
All Time Zone
Big Data Engineers Ready to Join Your Team
R
Certified
Rahul
Senior Data Engineer
- Databricks Certified Associate Developer for Apache Spark
- Confluent Certified Developer for Apache Kafka
- AWS Certified Data Engineer
- Apache Airflow Pipeline Specialist
Databricks Certified Associate Developer for Apache Spark
P
Certified
Priya
Data Integration Engineer
- Informatica PowerCenter Certified Developer
- Apache NiFi Flow Design and Cluster Management
- MuleSoft Certified Integration Associate
- Legacy ETL to Modern Pipeline Migration Specialist
A
Certified
Ankit
Big Data Engineer
- Cloudera Certified Associate Data Analyst
- Apache Spark Performance Tuning Specialist
- Hadoop Ecosystem and YARN Resource Management
- AWS EMR and Cloud-Native Data Processing
S
Certified
Sneha
Data Storage and Lakehouse Engineer
- DataStax Certified Apache Cassandra Developer
- Apache Iceberg and Delta Lake Table Format Specialist
- ClickHouse Cluster Architecture and Query Optimisation
- Data Lakehouse Design and Migration Specialist
V
Certified
Vikram
Big Data Architect
- Google Professional Data Engineer Certified
- Azure Data Engineer Associate Certified
- Apache Kafka and NiFi Enterprise Architecture
- Data Governance and Lakehouse Framework Design
Data teams worldwide are moving away from slow hiring cycles toward a model that matches capacity to demand. Here is what our big data staff augmentation services deliver.
Flexible Big Data Staff Augmentation Models Built
Around Your Needs
Dedicated Resource
A full-time big data engineer working exclusively on your project, under your direction, for as long as the work demands. Full capacity, full focus, zero split attention across other engagements.
Part-Time Support
Flexible big data staffing for teams that need specialist input on specific tasks, a Kafka tuning review, an Airflow DAG audit, and a storage architecture consultation, without a full-time commitment attached to it.
Project-Based Augmentation
A specialist or team engaged for a defined scope, timeline, and deliverables. The right big data outsourcing model for migrations, greenfield builds, and sprint-based workloads where the need has a clear endpoint.
Our Big Data Staff Augmentation Process
Requirement Discovery
We start with a focused consultation to understand your project scope, the specific tools and technologies involved, team structure, timeline, and preferred engagement model, remote, hybrid, or on-site.
Talent Matching
Our talent team scans our pre-vetted bench of big data engineers and matches candidates based on your exact technical requirements, tool depth, domain experience, and seniority level. No generalists presented for specialist roles.
Candidate Presentation
You receive shortlisted profiles within 48 to 72 hours. Each profile includes technical credentials, relevant project experience across the specific tools your stack runs on, and availability for your review and interview.
Interview and Selection
You interview the candidates and select the right fit. We facilitate the process, coordinate schedules, and provide any additional technical screening support you need.
Onboarding and Integration
Once selected, the engineer is onboarded to your systems, tools, and communication channels. Ksolves handles background checks, documentation, and initial briefings so your project timeline does not lose days to administrative setup.
Ongoing Governance and Support
We stay engaged throughout the contract. Regular check-ins, performance reviews, and a dedicated account manager ensure quality is maintained and any issues are resolved before they affect delivery.
Our Big Data Staff Augmentation Process
Our Proven Methodology for Seamless Talent Matching and Integration
Requirement Discovery
We start with a focused consultation to understand your project scope, the specific tools and technologies involved, team structure, timeline, and preferred engagement model, remote, hybrid, or on-site.
Talent Matching
Our talent team scans our pre-vetted bench of big data engineers and matches candidates based on your exact technical requirements, tool depth, domain experience, and seniority level. No generalists presented for specialist roles.
Candidate Presentation
You receive shortlisted profiles within 48 to 72 hours. Each profile includes technical credentials, relevant project experience across the specific tools your stack runs on, and availability for your review and interview.
Interview and Selection
You interview the candidates and select the right fit. We facilitate the process, coordinate schedules, and provide any additional technical screening support you need.
Onboarding and Integration
Once selected, the engineer is onboarded to your systems, tools, and communication channels. Ksolves handles background checks, documentation, and initial briefings so your project timeline does not lose days to administrative setup.
Ongoing Governance and Support
We stay engaged throughout the contract. Regular check-ins, performance reviews, and a dedicated account manager ensure quality is maintained and any issues are resolved before they affect delivery.
Big Data Staff Augmentation Across Industries
Our big data staff augmentation services are deployed across industries with varying compliance requirements, data volumes, and pipeline complexity.
Healthcare and Life Sciences
HIPAA-compliant architectures, HL7 and FHIR data integration, and high-availability clinical data pipelines require engineers who understand both the technology and the regulatory environment around it.
Financial Services
Real-time transaction processing, fraud detection pipelines, and regulatory reporting workflows demand engineers with financial data experience, not just big data experience.
Retail and E-commerce
High-volume clickstream data, inventory feeds, and personalisation engines need pipeline capacity that scales with seasonal demand. Hire Data engineers who have worked in retail data environments before.
Telecom
Network event data, call detail records, and subscriber analytics generate some of the highest data volumes in any vertical. Engineers placed by Ksolves understand the throughput demands and low-latency requirements of telecom data infrastructure.
Manufacturing and Supply Chain
IoT sensor data, production line telemetry, and supply chain event streams require pipelines built for reliability. Ksolves places engineers experienced in industrial data ingestion and edge-to-cloud architectures.
Logistics and Transportation
Route optimisation, fleet telemetry, and last-mile delivery data demand real-time processing with reliable pipeline uptime. In logistics, pipeline failure has a direct operational cost.
IT and SaaS
Product analytics, usage telemetry, and multi-tenant data architectures require senior engineers who operate autonomously. Ksolves places big data professionals who integrate quickly and require minimal ramp-up.
Big Data Insights from the Engineers Who Build It
Your Next Big Data Engineer Is One
Conversation Away
No long hiring cycles. Just the right specialist, matched to your stack, ready to deliver.
Frequently Asked Questions
Big Data staff augmentation is a flexible hiring model where certified data engineers integrate directly into your existing team to fill specific skill gaps, meet project deadlines, or scale capacity without the overhead of a permanent hire.
Shortlisted profiles are delivered within 48 to 72 hours of the requirement sign-off. The engineer can be onboarded and contributing within days.
Dedicated to full-time, part-time, and project-based engagements. Short-term or long-term, the model adapts to your project scope and timeline.
Ksolves engineers work across the full modern data stack, NiFi, Kafka, Spark, Cassandra, Iceberg, Hudi, Delta Lake, Airflow, Databricks, ClickHouse, Informatica, and cloud-native services on AWS, Azure, and GCP.
With staff augmentation, the engineer works under your direction within your team. You retain full control over priorities, processes, and delivery. Managed services hand that control to the vendor.
Yes. Ksolves’ big data staffing model is designed to flex with your project, add capacity during heavy build phases, and scale back during stabilisation.
Matching is done on specific tool experience and domain depth, not general data engineering background. You also interview and select the final candidate before anyone joins your project.
Yes. Ksolves provides 24×7 support across all time zones, ensuring your project has coverage when it needs it.
