Data Engineering Services

Build Reliable Data Infrastructure at Scale

Professional data engineering services that transform scattered data into reliable, scalable infrastructure. ETL pipelines, data warehouses, and real-time processing built for growth.

100+
Pipelines Built
10M+
Records Per Day
99.9%
Pipeline Reliability
60%
Cost Reduction

Your Data Engineering Lead

Aditya Aman

Data Engineering Lead with 10+ years experience building scalable data infrastructure. Specialized in ETL pipelines, data warehouses, and real-time processing systems. Built 100+ production pipelines processing millions of records daily.

Airflow Expertdbt Certified100+ Pipelines

The Challenge

Data Chaos Prevents Growth

Scattered Data Sources

Data lives in databases, APIs, spreadsheets, and SaaS tools. Without unified infrastructure, analysis is impossible and decisions are based on incomplete information.

Manual Data Processes

Teams waste hours on manual exports, transformations, and data cleanup. Errors creep in, data becomes stale, and analysts can't focus on insights.

Scale & Reliability Issues

As data volume grows, brittle processes break. Production pipelines fail, data becomes inconsistent, and trust in analytics erodes.

Our Solution

Production-Grade Data Infrastructure

We build reliable, scalable data infrastructure that automatically consolidates, transforms, and delivers your data where you need it.

Features

Enterprise-Grade Data Engineering

ETL Pipelines

Automated Extract, Transform, Load pipelines that consolidate data from multiple sources, apply business logic, and deliver clean data on schedule.

Data Warehousing

Modern data warehouse architecture using Snowflake, BigQuery, or PostgreSQL. Optimized schemas, partitioning, and query performance.

Real-Time Processing

Stream processing for real-time analytics and operational data. Kafka, Spark, and custom solutions for sub-second data latency.

Cloud Architecture

Cloud-native data infrastructure on AWS, GCP, or Azure. Auto-scaling, cost optimization, and multi-region deployment capabilities.

Data Quality & Monitoring

Automated data quality checks, anomaly detection, and pipeline monitoring. Alerts on failures, data drift, or quality issues.

Analytics-Ready Data

Transform raw data into analytics-ready tables optimized for BI tools, machine learning, and reporting. Star schema modeling and denormalization.

Our Services

End-to-End Data Engineering Solutions

Data Pipeline Development

Build custom ETL/ELT pipelines using Apache Airflow, dbt, Fivetran, or custom Python solutions. Scheduled batch processing or real-time streaming.

  • API integration & data extraction
  • Data transformation & enrichment
  • Automated scheduling & orchestration
  • Error handling & retry logic

Data Warehouse Architecture

Design and implement modern data warehouse solutions. Schema design, performance optimization, and cost management for Snowflake, BigQuery, or PostgreSQL.

  • Star/snowflake schema design
  • Data modeling best practices
  • Query optimization & indexing
  • Cost optimization strategies

Real-Time Data Processing

Build streaming data pipelines for real-time analytics, operational dashboards, and event-driven architectures. Kafka, Spark, and custom solutions.

  • Kafka streaming pipelines
  • Spark processing jobs
  • Event-driven architectures
  • Sub-second data latency

Data Quality & Governance

Implement data quality frameworks, monitoring, and governance. Ensure data accuracy, completeness, and compliance with automated validation.

  • Data quality validation rules
  • Anomaly detection systems
  • Data lineage tracking
  • Compliance & audit trails

Technology Stack

Modern Data Engineering Tools

We use industry-leading tools and frameworks proven to handle production workloads at scale.

Orchestration

Apache AirflowDagsterPrefectTemporal

Transformation

dbtPythonSparkPandas

Data Warehouses

SnowflakeBigQueryRedshiftPostgreSQL

Integration

FivetranAirbyteStitchCustom APIs

Streaming

Apache KafkaSpark StreamingFlinkKinesis

Cloud Platforms

AWSGCPAzureDocker

Our Process

How we build data infrastructure.

Build
Technical Implementation
Code changes
Create
Content Production
Write & optimize
Launch
Deploy
Go live

Case Study

Scaling E-Commerce Data Infrastructure

E-Commerce • Data Engineering

Processing 5M Records Daily Across 10 Data Sources

A fast-growing e-commerce company was drowning in manual data processes. Their team spent 20+ hours weekly on manual exports, transformations, and report generation. Data was stale, errors were common, and decision-making was slow.

5M
Records Per Day
10
Data Sources Integrated
95%
Time Savings

Solution

  • Built Airflow pipelines integrating Shopify, Google Analytics, Facebook Ads, email platform, and CRM
  • Implemented Snowflake data warehouse with optimized star schema for analytics
  • Created dbt transformation layer with data quality checks and automated alerts
  • Set up monitoring dashboards and Slack alerts for pipeline failures

Our Process

From Chaos to Reliable Infrastructure

1

Discovery & Architecture

Map all data sources, understand business requirements, and design data architecture. Define schemas, pipelines, and technology stack.

2

Data Warehouse Setup

Set up cloud data warehouse, design optimal schemas, configure security and access control, and establish best practices.

3

Pipeline Development

Build ETL pipelines connecting all data sources. Implement transformations, data quality checks, and error handling. Set up orchestration and scheduling.

4

Testing & Validation

Comprehensive testing of all pipelines, data quality validation, performance optimization, and historical data backfills.

5

Monitoring & Optimization

Set up monitoring dashboards, alerting, and documentation. Train your team and provide ongoing support for optimization and new pipeline development.

Industry Applications

Data Engineering for Every Industry

E-Commerce

Consolidate product, customer, and sales data for unified analytics and personalization.

SaaS

Integrate product usage, customer data, and billing for comprehensive business intelligence.

Marketing Agencies

Automate client reporting by consolidating data from advertising platforms, analytics, and CRMs.

Finance

Build compliant data pipelines with audit trails, encryption, and real-time risk monitoring.

Healthcare

HIPAA-compliant pipelines for patient data, claims processing, and operational analytics.

Logistics

Real-time shipment tracking, route optimization, and inventory management pipelines.

Pricing

Transparent Project-Based Pricing

Fixed-price projects with no surprises. Includes architecture, development, testing, and training.

Starter Pipeline

Basic ETL with 2-3 data sources

$12K project
  • 2-3 data source integrations
  • Basic data warehouse setup
  • Automated ETL pipelines
  • Data quality checks
  • 30 days post-launch support
Most Popular

Advanced Infrastructure

Complete data platform with 5+ sources

$28K project
  • 5+ data source integrations
  • Full data warehouse architecture
  • dbt transformation layer
  • Monitoring & alerting
  • Data quality framework
  • 90 days post-launch support

Enterprise

Custom platform with real-time processing

$60K+ custom
  • Unlimited data sources
  • Real-time streaming pipelines
  • Advanced data modeling
  • Multi-region deployment
  • Enterprise security & compliance
  • 6 months support & training

FAQ

Data Engineering Questions

FAQ

Frequently asked questions

ETL (Extract, Transform, Load) transforms data before loading into the warehouse. ELT (Extract, Load, Transform) loads raw data first, then transforms it in the warehouse. ELT is more common with modern cloud warehouses like Snowflake and BigQuery because they have powerful processing capabilities. We typically recommend ELT with tools like dbt for most modern data stacks, but ETL can be necessary when dealing with legacy systems or specific data quality requirements.
Timeline depends on complexity. A basic pipeline with 2-3 data sources takes 3-4 weeks. More complex infrastructure with 5+ sources, transformation logic, and data quality frameworks takes 6-8 weeks. Enterprise solutions with real-time processing and multiple environments take 10-12 weeks. We provide detailed timelines after the discovery phase.
Snowflake, BigQuery, and Redshift are all excellent choices. Snowflake offers the best balance of performance, features, and ease of use. BigQuery is ideal if you're already in the Google Cloud ecosystem and need tight GCP integration. Redshift makes sense if you're heavily invested in AWS. For smaller projects or tight budgets, PostgreSQL can work well. We help you evaluate based on your specific requirements, existing infrastructure, and budget.
We implement comprehensive error handling, monitoring, and alerting in all pipelines. If a pipeline fails, you receive immediate notifications via Slack, email, or PagerDuty. Pipelines include automatic retry logic for transient failures. We maintain detailed logs and monitoring dashboards showing pipeline health. Most issues can be diagnosed and resolved quickly, and we provide support to get pipelines back online.
Yes. We specialize in migrating from legacy systems, on-premise databases, or outdated cloud infrastructure to modern data warehouses and pipelines. Migration projects include data validation to ensure no data loss, parallel running of old and new systems during transition, and comprehensive testing. We minimize disruption and typically complete migrations in 4-8 weeks depending on data volume and complexity.
Yes. All projects include post-launch support (30-90 days depending on package). This covers bug fixes, pipeline optimization, and training. After the initial support period, we offer monthly maintenance packages that include monitoring, updates, new pipeline development, and ongoing optimization. Many clients retain us for continued data engineering work as their needs evolve.

Ready to Build Reliable Data Infrastructure?

Let's discuss your data challenges and design a scalable solution.