Data Engineering Services
Enable AI and advanced analytics by building scalable data pipelines and modern data platforms that deliver accessible and high-quality data.
BUILD A FUTURE-PROOF DATA FOUNDATION FOR ANALYTICS & AI
Drive faster decision-making, improve operational efficiency, and enable scalable AI adoption with trusted enterprise data. We design and build AI-ready data platforms that unify and organize data across systems, automate data pipelines, and improve data quality to support reliable reporting, analytics, and greater business agility. Our experience working with large enterprises in highly regulated industries ensures solutions are built with security, governance, and compliance in mind.
You are in great company
Data engineering challenges we solve for enterprises
Fragmented data across multiple systems
Build unified data integration pipelines that connect legacy systems, on-premise setups, and cloud platforms into a single, consistent data layer, to provide visibility across every source.
Data that is not AI & analytics ready
Standardize, cleanse, and structure your data to meet the quality thresholds required for AI and analytics workloads, so the models and reports are built on data you can trust.
Slow & unreliable data pipelines
Re-engineering brittle and manual pipelines into automated, monitored, and resilient data flows that deliver accurate data on time without the rework.
Legacy infrastructure that cannot scale
Modernizing your legacy data infrastructure to handle modern scale and AI workloads, eliminating performance bottlenecks and reducing the operational complexity.
Limited in-house data engineering capacity
Employ experienced engineers to augment your team, freeing internal resources to focus on strategic initiatives while accelerating the delivery of new data capabilities.
Rising cloud & data infrastructure costs
Audit and re-architect inefficient data environments to eliminate duplication, optimize cloud resource usage, and improve return on your data infrastructure investments.
Our data engineering services
We transform unusable data from countless sources to trusted analytics-ready assets through AI-native automations, data pipelines, and platform modernization. Our data engineering services include the following.
Data architecture consulting
Efficiently implement and maintain high-performing solutions with scalable data architecture that boosts performance and supports business outcomes.
AI & GenAI data engineering
Improve AI performance and accelerate time-to-value by building AI-ready data foundations that support automation and insight-driven decision-making.
Data governance & quality engineering
Ensure trusted data for AI, analytics, and business operations by defining data standards, access controls, monitoring processes, and quality checks.
Data pipeline development (ETL/ELT)
Design scalable ETL & ELT pipelines that move and transform data across systems. Apply business rules and transformation logic to prepare data for analytics, reporting, and AI initiatives.
Data lake & data warehouse implementation
Architect and deploy modern data warehouses and lakes on Snowflake, Databricks, BigQuery, and Redshift that are designed for performance, scalability, and analytics readiness.
Cloud data platform engineering
Enable efficient data processing and storage for analysis with cloud-native data tooling on platforms like AWS, Azure, and GCP, automating the heavy lifting of operations.
Data integration & ingestion
Unify data from various sources, including APIs, databases, SaaS tools, and legacy systems. Real-time and batch ingestion with automated cleaning, error handling, and analysis.
Data modernization & migration
Modernize and securely move data from on-premise or legacy infrastructure to cloud-native, hybrid or multi-cloud environments with minimal downtime and business disruption.
DataOps & pipeline observability
Keep data pipelines healthy with CI/CD automation, real-time monitoring, and self-healing mechanisms that resolve issues before they impact your business.
How we deliver data engineering services
We structure our data engineering engagements around five steps that align data infrastructure with business priorities and measurable impact.
Identify high-value opportunities
Evaluate your existing data ecosystem and architecture to identify gaps, bottlenecks, and high-value opportunities.
Create a scalable foundation
Design a scalable data architecture and a prioritized roadmap that aligns data engineering with your operational and AI priorities.
Deliver trusted data
Develop production-ready pipelines, integrations, and data workflows that deliver clean, usable data.
Deploy & enable business value
Release validated pipelines through CI/CD, confirm data accuracy, performance, and system integration before go-live.
Drive continuous growth
Maintain and optimize data pipelines to ensure reliability and support analytics, AI, and growing workloads.
Why choose 10Pearls for data engineering consulting services
AI & advanced analytics expertise
Proven experience enabling AI, ML, and statistical models to automate data processing, predict trends, and generate actionable insights.
Experience in highly regulated industries
Drawing on years of experience across regulated industries, we help organizations manage risk, maintain compliance, and get greater value from their data.
Strategy-first consulting approach
Maximize the return on data investments with roadmaps that align engineering initiatives to measurable business outcomes.
End-to-end engineering depth
With deep experience in delivering enterprise data engineering, we build pipelines and optimize workflows across the full data lifecycle.
Global delivery with enterprise scale
Accelerate delivery and scale transformation initiatives with access to global talent, specialized expertise, and enterprise-grade execution.
Data engineering services by industry
Every industry has its own data complexity. Our data engineering consulting services are tailored to your sector’s regulatory, operational, and analytical requirements.
Healthcare
Connect patient, research, and operational data through secure pipelines to support HIPAA-compliant analytics and reporting systems.
Financial services
Build secure data systems that support reporting, risk management, and better decision-making.
Media
Develop customer and network data pipelines together to improve service quality and customer retention.
Retail & consumer goods
Connect sales, customer, and inventory data through scalable pipelines to improve forecasting and personalization.
Technology
Modernize data systems to support product analytics, user insights, and AI applications.
Telecommunications
Build scalable pipelines to ingest and process massive volumes of network, billing, and customer data in real time.
Energy
Transform complex telemetry from power grids and oil rigs into actionable insights, enabling predictive maintenance, and renewable energy integration.
Real estate
Enhance decision making by integrating scattered property information to replace manual reporting with automated, data-driven platforms.
Transportation
Empower organizations to optimize fleet routes, predict maintenance, streamline supply chains, and reduce overall operational costs.
Modern data engineering stack we work with
Case Studies
case study - technology
Data Intelligence Platform
Accelerating public health emergency response with an enhanced, secure platform enabling real-time data sharing.
case study - TECHNOLOGY
Agile Cloud Infrastructure
Improving operational agility, reducing cost, and optimizing resource management with a modernized, cloud-based infrastructure.
case study - TECHNOLOGY
Intelligent Data Analytics Platform
Driving better insights and decision-making through a modernized analytics platform, maximizing the impact of data.
case study - TECHNOLOGY
Centralized Cloud Data Warehouse
Advancing student support and success with a cloud-based system which enabled identification of at-risk students.
FAQs about data engineering services
What is the difference between data engineering and data modeling?
Data engineering focuses on building and managing the systems, pipelines, and infrastructure that collect, move, and process data. Whereas data modeling focuses on organizing and structuring that data, so it is easy to understand, access, analyze, and use across business applications and reporting tools.
What do data engineering services include?
Data engineering services help businesses collect, organize, move, and manage data from different systems. They include building data pipelines, improving data quality, connecting platforms, setting up cloud data systems, and preparing data for reporting, analytics, and AI applications.
How do you handle data integrations from diverse sources, such as legacy systems?
We connect and combine data from different environments, including modern databases, APIs, file transfers, and legacy systems with custom or outdated protocols. Our ETL and ELT pipelines are designed to reliably extract, transform, and load data while ensuring its accuracy, consistency, and integrity across all sources.
How do you ensure the security and compliance of data pipelines?
We prioritize data security and regulatory compliance at all stages of the pipelines. Our approach includes strong governance practices aligned with standards like GDPR, HIPAA, and SOC2. We safeguard sensitive information through encryption, data masking, and strict access controls. In addition, we implement real-time monitoring and observability to continuously track, detect, and protect data across its lifecycle.
Modernize your data foundations
Whether you’re modernizing legacy infrastructure, migrating to the cloud, or building a new data platform from scratch, our data engineering services meet you where you are.