Our engineers work across the core platforms teams rely on for ingestion, storage, distributed compute, and analytics handoff. The visual stack below keeps the existing asset-driven technology section in place while the page copy below goes deeper into delivery scope.
Big Data
Cassandra
Cloudera
Ingestion
Oracle
Retail
Spark
SQL ServerBig Data Development Services Our Engineers Deliver
Big data hiring works best when the engineer can see the entire path from ingestion to governed storage to business-ready output. That is the mindset we optimise for when matching talent.
Data pipeline design and development (ETL/ELT)
We build data pipelines that move reliably from source systems to lakes, warehouses, and downstream analytics tools. That includes ingestion design, transformation logic, quality checks, lineage considerations, and cost-aware orchestration so pipelines remain operable after the first release.
Apache Hadoop ecosystem — HDFS, MapReduce, Hive, HBase
For teams with legacy Hadoop estates or on-prem data processing requirements, Iyrix engineers can stabilise, optimise, and extend the cluster instead of forcing a disruptive rewrite too early. We work across storage, distributed compute, query layers, and migration planning.
Apache Spark for real-time and batch processing
Spark is still one of the most practical tools for large-scale transformation and compute-heavy analytics. Our developers use it for batch ETL, structured streaming, feature generation, and jobs that need a better balance of speed and maintainability than older ETL approaches provide.
Apache Kafka for event streaming and message queuing
When products need reliable event flow at scale, Kafka gives engineering teams a strong backbone for stream processing, service integration, and asynchronous workloads. We help with topic design, schema discipline, retention policy, connectors, and consumers that can handle production load cleanly.
Cloud data warehouses — AWS Redshift, Google BigQuery, Snowflake
Modern data teams increasingly need warehouse design that supports BI, finance, product analytics, and machine learning with one trusted model. Iyrix engineers help with modelling, partitioning, cost governance, permissions, and loading patterns across the warehouse platforms most teams already use.
Data lake architecture and governance
Data lakes create leverage only when storage design, metadata, permissions, retention, and lineage are planned from the beginning. We design lake structures that support both operational flexibility and compliance instead of creating a dumping ground that is hard to trust later.
ML pipeline integration and feature engineering
Big data engineering often sits directly upstream of machine learning. Our developers build the feature pipelines, batch and streaming transforms, data contracts, and orchestration patterns needed for ML teams to work from consistent, production-grade data rather than one-off notebook extracts.
Why Companies Hire Big Data Developers Through Iyrix
Hiring for data at scale is usually less about generic coding ability and more about how an engineer behaves under throughput, reliability, and governance pressure.
Specialists with production-scale big data experience (1B+ rows)
We prioritise engineers who have dealt with very large datasets, long-running jobs, event volume, and warehouse cost pressure in real delivery environments. That tends to matter far more than textbook familiarity with tooling names.
Pre-vetted on real distributed systems projects
Our screening looks for experience with pipeline reliability, debugging in distributed environments, schema evolution, orchestration failures, cloud permissions, and the handoff problems that appear between product, analytics, and platform teams.
14-day risk-free developer replacement guarantee
If the first match is not right, we replace quickly so you do not lose momentum while trying to fix a hiring decision. That helps technical buyers move faster without absorbing unnecessary staffing risk.
Tell us your current data scale, platform stack, and processing bottlenecks and we will match you with big data engineers who fit the actual challenge.
This is especially useful for teams modernising ETL, scaling event pipelines, cleaning up warehouse performance, or shifting from self-managed tools to cloud-native data services.
Most clients receive 3 matched developer profiles within 24 hours and can start within 48 hours.
Big Data Technology Stack
The right stack depends on your data velocity, compliance needs, cloud strategy, and who needs to consume the output. We work across the layers most teams combine in production.
Processing — Apache Spark, Hadoop MapReduce, Flink
We choose processing engines based on latency expectations, compute cost, and operational complexity. Spark remains a strong default for many teams, while Flink or cloud-managed stream processing may be better when event-time semantics and lower-latency pipelines matter.
Storage — HDFS, Amazon S3, Google Cloud Storage, Azure Data Lake
Storage decisions affect everything from cost and retention to governance and downstream query patterns. Our engineers help design object-storage and lake layouts that stay usable for analytics, ML, and compliance over time.
Streaming — Apache Kafka, AWS Kinesis, Azure Event Hubs
Event systems are only useful when they remain observable and stable under pressure. We support topic or stream design, replay strategies, schema control, consumer scaling, and service integrations that reduce event chaos as traffic grows.
Warehousing — Snowflake, BigQuery, Redshift, Databricks
Warehouse platform choice shapes cost, query speed, governance, and modelling workflows. Iyrix works with the main cloud warehouse platforms and helps teams structure data for dashboards, self-serve analytics, and cross-functional reporting.
Orchestration — Apache Airflow, Luigi, Prefect
Pipeline reliability depends as much on orchestration quality as on transformation logic. We implement DAGs, retries, monitoring, backfills, and run-time guardrails so the pipeline layer is dependable for both engineering and analytics consumers.
Languages — Python, Scala, Java, SQL
Language choice usually follows the surrounding platform and talent mix. Our big data developers are commonly strongest in Python and SQL, with Scala and Java used where Spark, JVM tooling, or long-standing enterprise estates call for them.
| Stack layer | Common platforms | What our engineers optimise for |
|---|---|---|
| Ingestion | Kafka, Kinesis, Event Hubs, custom APIs | Throughput, delivery guarantees, schema control, replay strategy |
| Compute | Spark, Hadoop, Flink, cloud-native transforms | Performance, maintainability, cost per job, observability |
| Storage | S3, GCS, Azure Data Lake, HDFS | Partitioning, access control, retention, governance |
| Serving | Snowflake, BigQuery, Redshift, Databricks SQL | Query performance, modelling quality, analytics usability |
Big Data Developer vs Data Scientist vs Data Analyst — What Do You Need?
A lot of hiring confusion comes from using data roles interchangeably. In practice, the value of each role sits in a different part of the workflow. A big data developer is usually the right hire when the biggest problem is moving, storing, processing, or governing large-scale data reliably. A data scientist becomes more valuable when modelling, experimentation, and predictive output are the bottleneck. A data analyst is usually the right hire when the business mainly needs reporting, slicing, dashboards, and interpretation for decision support.
| Comparison factor | Big Data Developer | Data Scientist | Data Analyst |
|---|---|---|---|
| Primary skill | Distributed systems, pipelines, warehouse and lake architecture | Statistical modelling, experimentation, machine learning | Reporting, business analysis, dashboard interpretation |
| Tools | Spark, Kafka, Airflow, Hadoop, Snowflake, BigQuery | Python, notebooks, ML frameworks, feature stores | SQL, BI tools, spreadsheets, dashboard platforms |
| Output | Reliable data movement, clean storage, production-ready datasets | Models, forecasts, segmentation, predictive insights | Reports, KPIs, trend analysis, stakeholder-ready dashboards |
| When to hire | When scale, latency, reliability, and data plumbing are the core problem | When you need predictive models or experimentation on top of stable data | When the business mainly needs analysis and visibility from existing data |
Big Data Project Case Studies
The best signal for a big data engineer is whether they have already improved throughput, reliability, or compliance in a live environment. These examples show the types of outcomes Iyrix teams are matched to deliver.
E-commerce platform: Kafka pipeline processing 50M events per day
An ecommerce client needed to unify clickstream, order, and fulfillment events across regional systems. Iyrix engineers redesigned the Kafka ingestion layer, improved consumer resilience, and reduced downstream lag dramatically. The new pipeline handled 50 million events per day with more predictable latency and cleaner analytics handoff for product and marketing teams.
Financial services: Spark cluster replacing legacy ETL — 10x performance gain
The client was still running a slow legacy ETL process that could not keep up with overnight reporting needs. We replaced the older transformation flow with Spark-based processing and reworked the storage layout to improve job efficiency. The final system delivered a 10x performance gain and shortened daily data availability windows for operations and finance users.
Healthcare analytics: HIPAA-compliant data lake on AWS
A healthcare analytics team needed a lake architecture that could support reporting and machine learning while respecting PHI controls. Iyrix engineers designed an AWS-based lake with encrypted storage, role-based access, logging, and governed processing zones. The result reduced manual data-prep effort by 42% and gave analysts a more secure path to trusted datasets.
Flexible Big Data Engagement Models
Data infrastructure work changes shape depending on how much architecture support you need and whether the problem is ongoing platform delivery or a contained build.
Dedicated big data engineer — full-time, your team
This model is best when you already know the platform direction and need one engineer embedded in your sprint cadence to own pipelines, platform cleanup, warehouse work, or event processing improvements over time.
Data team augmentation — architect, engineer, analyst
When your data challenge spans platform design, implementation, and business-facing reporting, a small mixed team usually creates the best momentum. We can augment your existing organisation with the exact roles that are missing.
Project-based — fixed scope data infrastructure build
If the problem is tightly bounded, such as building a new ingestion pipeline, warehouse migration, or data lake foundation, fixed-scope delivery gives you commercial predictability without hiring full-time immediately.
How to Hire a Big Data Developer Through Iyrix — 3 Steps
We keep the process short because most teams asking for big data help already have an infrastructure problem they need to solve quickly.
Step 1 — Describe your data scale, stack, and pipeline requirements
Tell us what cloud or on-prem environment you are in, what tools you use today, how large the data footprint is, and what problems are hurting the business most right now.
Step 2 — We match you with 3 vetted big data engineers within 24 hours
We shortlist based on platform fit, communication style, seniority, and whether you need more warehouse depth, more streaming depth, or more distributed systems experience.
Step 3 — Interview, choose, and start within 48 hours
Once you pick the engineer or team, we move into onboarding immediately. Most clients start with a clear pipeline, migration, or stabilisation backlog so the first weeks generate visible value.
Frequently Asked Questions About Hiring Big Data Developers
How much does it cost to hire a big data developer?
Big data developer rates through Iyrix typically range from $45-$95 per hour depending on platform depth, streaming complexity, and whether the role leans more toward Spark, Kafka, or warehouse architecture. Dedicated monthly engagements are available when long-running pipeline or platform work is the priority.
What is the difference between a big data developer and a data engineer?
The terms overlap heavily. Big data developer is often used when distributed systems and scale-heavy processing are central. Data engineer is often used when pipeline design, modelling, and data platform ownership are the focus. Iyrix can match for either profile depending on your need.
Do I need Hadoop or can I use cloud-native services instead?
For many new projects, cloud-native services are easier to operate and faster to deliver than self-managed Hadoop. Hadoop still makes sense in some on-prem or legacy-heavy environments. We usually recommend based on the economics and operational constraints of your current estate, not just trend preference.
Can your developers work with our existing AWS / Azure / GCP setup?
Yes. Our big data engineers work across AWS, Azure, and Google Cloud data services, including warehouse, storage, orchestration, and event-streaming layers. We can fit into the platform choices you already have instead of forcing a full-stack replacement conversation.
How do you ensure data security and compliance (GDPR, HIPAA)?
We design for encryption, access control, masking of sensitive data, auditability, and well-defined storage zones. Our developers have experience supporting HIPAA- and GDPR-aware data workflows where governance cannot be treated as an afterthought.
What happens if the developer is not the right fit?
We replace your big data developer within 14 days at no extra cost. That allows you to move quickly on hiring without accepting long-term risk if the initial match does not work in practice.
Ready to scale your data infrastructure? Get matched today.
Whether you are modernising ETL, rebuilding event pipelines, designing a governed data lake, or trying to lower warehouse cost without losing reporting quality, Iyrix can help you move faster.
Ready to hire a big data developer? Get matched in 24 hours.