Data Science & Data Engineering
Data science and data engineering services — from ETL pipelines and data warehouses to analytics dashboards, predictive models, and ML-ready platforms. Turn raw data into decisions your leadership can trust.
Spreadsheets and siloed databases hide patterns that drive revenue and efficiency. Hiraba builds modern data stacks — ingestion, transformation, warehousing, visualization, and optional ML layers — integrated with your applications.
From raw data to reliable insights
Good analytics needs clean pipelines first. We design lakehouse/warehouse architectures, batch and streaming ETL, data quality checks, and BI dashboards — then layer data science and ML where they deliver measurable ROI.
Key benefits
- Single source of truth for metrics
- Automated ETL & data quality
- Real-time & batch analytics
- Executive dashboards & reporting
- ML-ready feature stores
- Scalable cloud data platforms
Technologies we use
- React / Angular / Vue / Svelte
- Node.js / Python / Java / Go / Rust / .NET / PHP
- Mobile (iOS / Android / React Native / Flutter)
- DevOps / AWS / Azure / GCP
What we deliver
- Data pipeline & ETL development
- Data warehouse / lakehouse setup
- BI dashboards (Power BI, Metabase, custom)
- Data quality & governance frameworks
- Predictive analytics & ML models
- dbt, Airflow, Spark workflows
Industries we serve
- Manufacturing
- Retail & E-Commerce
- FinTech
- Healthcare
- SaaS & Products
- Logistics
Pricing & engagement models
Transparent pricing tailored to your scope. All plans include a free consultation — contact us for an exact quote.
Data Foundation
$5,000+
Pipeline + warehouse setup- Source system audit
- ETL pipeline design
- Warehouse setup
- Core dashboards
- Documentation
Analytics Platform
$12,000+
Full data platform- Multi-source ingestion
- dbt transformations
- BI dashboards
- Data quality checks
- 3 months support
Data Science+
Custom
ML & advanced analytics- Predictive models
- Feature engineering
- MLOps pipelines
- A/B testing frameworks
- Dedicated data team
Why choose Hiraba
Pipeline before models
We fix data quality and warehousing first — so analytics and ML stand on solid ground.
Cloud-native
Snowflake, BigQuery, Redshift, Databricks — right-sized for your volume and budget.
Integrated delivery
Data pipelines connect to your ERP, SaaS, and apps — not isolated spreadsheets.
Frequently asked questions
Python, SQL, dbt, Airflow, Spark, Kafka, Snowflake, BigQuery, PostgreSQL, and Power BI / Metabase.
Yes — we automate manual reports into live dashboards with validated pipelines.
Yes — after data foundation is solid; classification, forecasting, and recommendation models.
Yes — streaming with Kafka, Flink, or cloud-native services where needed.
Automated tests, schema validation, anomaly alerts, and documented lineage.
Operational Efficiency
Automated data pipelines replace manual exports and spreadsheet merges — delivering accurate KPIs daily. Leaders see production, sales, and operations metrics from one trusted platform instead of conflicting reports.
Data platform capabilities
- ETL / ELT pipelines
- Data warehouse / lake
- BI dashboards
- Data quality & governance
- ML & predictive analytics
01 Background
Teams drown in exports from ERP, CRM, and ads platforms — spending days reconciling numbers that should be automatic.
Hiraba builds operational software with rich data daily. We apply the same rigor to analytics platforms — so insights match what happens on the shop floor and in your apps.
02 The Challenges
Dirty data, missing lineage, and one-off SQL scripts make executives distrust every dashboard.
Data science initiatives fail without engineering — models trained on inconsistent snapshots never reach production.
Data & analytics challenges
- Siloed spreadsheets
- No data quality checks
- Slow manual reporting
- ML without reliable pipelines
03 The Solution
We implement governed pipelines with tests, documentation, and incremental loads — one metrics definition across the business.
Phased delivery: warehouse and dashboards first, then data science where ROI is proven — with MLOps for production models.
Our Process
How we deliver data engineering
A proven four-step approach — from discovery to production deployment and ongoing support.
Data Audit
Map sources, quality issues, key metrics, and stakeholder reporting needs.
Architecture
Design warehouse/lakehouse, pipeline patterns, governance, and dashboard specs.
Build Pipelines
ETL/ELT jobs, transformations, quality checks, and incremental loads.
Visualize & Iterate
Dashboards, self-serve analytics, ML layers, and ongoing optimization.
Projects delivered
Custom software, SaaS, and mobile apps for clients worldwide.
Days to MVP
Rapid prototyping for founders and enterprises validating new ideas.
Founded
Over a decade of software delivery from Ahmedabad, India.
Ready to get started?
Book a free consultation. We'll understand your goals and recommend the right engagement model — fixed-price, dedicated team, or phased delivery.
Book Free Consultation