Big Data Engineering Services

We deliver reliable, scalable Big Data infrastructure that keeps pace with your growth. No more broken integrations, slow queries, and siloed data that cost millions in missed decisions every year. Only professional Big Data engineering services, perfectly suited for your needs.

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What You Gain from Day One

Faster decisions

Key reports that used to take days are ready in hours, giving your analysts actionable insights from current data, not yesterday's exports or last week's snapshot.

Trusted KPIs

With our data science engineering services, every metric has one definition: agreed on, tested, and documented. No more debates. Clean, trusted metrics are the foundation of data-driven decision-making.

Fewer incidents

Efficient data pipelines are monitored with clear owners and defined SLAs (response time commitments). Issues surface before they reach the business.

Lower cost to serve

Warehouse and pipeline spend is optimized. You stop paying for redundant compute, bloated queries, and infrastructure you've outgrown.

Safer data

Data security is built in. Access is controlled and logged. When a compliance review like GDPR, SOC 2, HIPAA comes, you have the audit trail ready and the answers on hand.

AI-ready data

Our data engineering services focus on clean, versioned, reproducible datasets. Whether you're building models now or planning ahead, your data infrastructure won't be the thing that holds AI projects back.

Industry Recognition

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Data Engineering Services We Provide

Your data foundation shapes everything built on top of it. Our engineers analyze your existing systems, then design and optimize database and data warehouse platforms that fit your business.

Our data integration engineering services cover the entire data lifecycle, from tool selection and system connections to the architecture needed to keep your data accessible, accurate, and secure.

We transform raw data from streaming and batch sources, including structured and unstructured formats, integrating it cleanly with legacy databases or cloud systems.

Our engineers handle data transformation and cleansing to ensure quality and consistency at every stage of your pipeline.

We design and implement data architectures built for large-scale data processing, handling petabytes reliably and securely. This data engineering service integrates with your existing systems, supports big data analytics, and scales as your data volumes grow.

If your current setup struggles to keep up with growing data volumes, we replace the bottlenecks with a scalable data infrastructure that holds up under real load.

We assess your current setup and define a data strategy and cloud data architecture that reduces infrastructure costs and improves business agility.

We start with a clear roadmap and guide your migration end-to-end. You get a cloud platform that's solid from day one and sustainable as your needs change.

A structured data warehouse improves data accessibility by putting all your data in one place, so you can query, report on, and analyze it without hunting across siloed systems. Centralizing your data eliminates duplication and improves data management and quality across the board.

We build automated data pipelines using proven tools for modeling, querying, and analysis, and improve query performance as you grow.

We implement big data infrastructures that extend your current IT setup or build new ones where needed. Our team has experience loading data into relational, non-relational, NoSQL, data lakes, and cloud storage systems.

We match the storage solution to your data usage patterns, availability, volume, velocity, and type. As your business grows, storing data at scale becomes simpler, and your infrastructure keeps pace.

We move your data from on-premises legacy systems to cloud environments or a new target platform. Our team builds production-grade, repeatable pipelines that streamline data workflows, moving, transforming, and storing your data reliably.

We process data in batch and real time using cloud orchestration and pipeline tools to ensure efficient data flow, keeping business continuity intact and downtime to a minimum.

We build custom dashboards and reports that pull from multiple sources into a single, unified view. Visual representations deliver data-driven insights, helping you spot patterns, trends, and anomalies quickly so you can act on what the data shows.

We handle a wide range of data types, including structured and unstructured, from cloud and on-premises sources alike.

We design and implement schemas tailored to your requirements, data sources, and data types. Rigorous data quality management, testing, and validation ensure data integrity and consistency throughout.

If you need ongoing flexibility, we also offer data engineering as a service, handling schema monitoring and adjustments as your business needs evolve.

Data Challenges We Solve

Most data problems look unique on the surface, but the root causes repeat across companies of all sizes and industries. Here are the ones we work on most often.

Data scattered across disconnected systems

When your data lives in separate tools, databases, and platforms, getting a complete view of your business takes manual effort that shouldn't be necessary. Reports get consolidated by hand, numbers don't match between teams, and decisions get delayed. Our fix: We design unified data architectures with clean connectors, canonical data models, and integration pipelines that give you one reliable source of truth across all your systems.

Unreliable data you can't act on

If your team doesn't trust the data, they won't use it. Duplicate records, inconsistent formats, and missing values are common in companies that have grown quickly or merged systems over time. Our fix: We build data processing pipelines that maintain high data quality, cleansing, validating, and standardizing your data before it reaches anyone who needs to act on it. Governed metric definitions and data lineage documentation help your teams align on the same numbers.

Infrastructure that can't keep up with data growth

What works at 10 gigabytes often fails at 10 terabytes. Poorly designed pipelines and storage systems become bottlenecks as volumes grow. Our fix: We build modern data architectures using Apache Spark, Kafka, and Amazon Redshift that are designed to scale from the start, not patched as problems appear.

Legacy systems slowing down your move to the cloud

Migrating data from on-premises systems to the cloud is complex when those systems were never built for it. A rushed migration risks data loss, downtime, and quality issues. Our fix: We plan and run cloud migrations in structured phases using repeatable pipelines that protect data integrity at every step.

No clear visibility into what your data is telling you

Having data and understanding it are two different things. If your team can't read what the data shows, it doesn't improve decisions. Our fix: We build custom dashboards and visual reports that connect multiple sources into a single view, so patterns and issues are visible to the people who need to act on them.

Slow pipelines blocking real-time decisions

Batch processing that runs overnight is not fast enough for businesses that need to respond in hours, not days. Our fix: We design advanced data pipelines for real-time and near-real-time processing using tools like Kafka and AWS Glue, so your data reaches the right systems and people without unnecessary delay.

Revenue attribution you can't rely on

Broken funnel tracking, inconsistent UTMs, and unclear CAC/LTV by segment make it hard to know which channels drive growth. Marketing, sales, and product teams end up working from different data and drawing different conclusions. Our fix: Our data engineering solutions build end-to-end attribution-ready pipelines with standardized event tracking and a unified cross-team data model, so your growth teams have one clear picture of what's driving revenue.

Cloud data costs rising faster than the value you're getting

Warehouse bills spike. Queries run inefficiently. Duplicate pipelines store more raw data than you'll ever use. Our fix: We review your data infrastructure and apply partitioning, clustering, caching, and retention policies to bring costs in line with actual usage and cut what isn't earning its place.

Compliance and privacy risk you can't fully account for

PII and PHI spread across systems, unclear access controls, and audit gaps create real exposure under GDPR, HIPAA, and SOC 2. Our fix: We implement data access controls, masking, tokenization, audit-ready logging, and retention governance. Privacy-by-design principles go into the architecture from the start, not added as a fix later.

AI and Machine Learning ambitions blocked by poor data quality

Inconsistent features, non-reproducible training sets, and no real-time signals make it hard to build reliable models. Our fix: We build ML-ready datasets, feature pipelines, and offline/online consistency layers so your data science team works with reproducible, production-grade training data.

The Data Foundation Your AI Needs

We work as your data engineering service provider to sort out the foundation, so your AI work has reliable input to run on. As part of our end-to-end data engineering consulting services, we cover everything from ingestion to model-ready output.

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No data silos and inconsistency

Fragmented sources get connected and aligned. Every team reads from the same data.

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No missing ML and analytics pipelines

We build pipelines from raw ingestion to model inference, enabling advanced analytics. No manual handoffs, no gaps.

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Data quality and trust

Validation, lineage tracking, and observability go into every layer. You can see where data comes from and whether it's reliable.

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Real-time data access

Where your AI workloads need fresh input, we set up streaming to replace batch delays.

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No gaps in governance and scalability

Data governance, access controls, data contracts, and compliance requirements are handled at the architecture level before they become problems.

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What you end up with

A clean, connected, production-grade data layer – one that accelerates model performance, shortens time-to-insight, and powers products that get smarter over time.

Our Cases of Software Product Development Service

“TechMagic stood out for their systems and delivery. The features they added helped drive a 700% increase in active users.”

Blake Cassidy

CEO

Micro investing app with rewards and automated portfolio building

Check how we delivered new investment flows, token mechanics, and portfolio features that supported a 700% increase in active users.

Bamboo case study

Personality-based career matching for job seekers and employers

Explore how we built a swipe-based quiz experience with gamification and FitScore logic that helps nearly 3 million users understand their strengths and helps companies hire for real culture alignment.

Good&Co case study

E-commerce analytics platform for Amazon growth

See how we rebuilt the product with a modern UI and scalable AWS architecture that increased platform performance 15 times and enabled independent client use.

Acorn-i case study

Enterprise process visualization platform with Salesforce integration

Discover how we delivered a secure and scalable process mapping solution with Salesforce integrations and enterprise-grade architecture built for AppExchange compliance.

Elements.cloud case study

Digital platform for medical form creation and management

Read how we built a real-time form configurator with an accessible interface that reduces documentation errors and saves time for healthcare professionals.

Tiro.health case study

“TechMagic takes time to understand customer needs, so the team stays focused on what matters and moves faster.”

Axel Vanraes

Tech Co founder

All case studies

Our Workflow

Discovery and scoping

In our end-to-end data engineering consulting services, we start with understanding your current data environment. Our engineers audit your data sources, data systems, and existing workflows to identify gaps, bottlenecks, and opportunities.

You get a clear picture of where things stand before we change anything.

Architecture and planning

We translate the assessment findings into a step-by-step plan. The roadmap covers architecture decisions, tooling choices, migration paths, and delivery timelines.

You know what we're building, in what order, and why each decision was made.

Building and deployment

Our engineers handle data pipeline development and deploy the agreed solution — whether that's pipelines, storage systems, data models, or a full migration.

We work in structured phases so you can review progress and give feedback along the way.

Testing and optimization

Once the initial build is live, we measure how it performs against your goals. If something needs tuning, we fix it.

As your data volumes and business needs change, we adjust the solution to stay aligned.

Maintenance and support

We monitor your data infrastructure on an ongoing basis and resolve issues before they affect your operations.

You get a long-term engineering partner, not just a one-time delivery.

Data Engineering Technologies We Use

Amazon AWS

Python

Snowflake

MongoDB

BigQuery

Amazon Redshift

Apache Spark

Airflow

Hadoop

Kafka

AWS Athena

AWS Glue

Let’s talk real numbers!

Contact our experts to learn how our data engineers can enforce your business today

Industries

We deliver digital data engineering services across sectors where data complexity is high, and the cost of getting it wrong is higher.

Healthcare

Healthcare data is sensitive, heavily regulated, and often fragmented across systems that were never designed to talk to each other. We work with the full stack: EHR integrations, HL7 and FHIR standards, clinical data pipelines, and compliance requirements like HIPAA. You get data that clinical and operational teams can actually use, without creating new risk in the process.

FinTech

Financial data moves fast and carries real consequences when it's wrong. Our big data analytics services handle high transaction volumes, support real-time fraud signals, and stay audit-ready for regulatory reporting. And all of that without slowing down the product.

EdTech

Learning platforms generate large volumes of behavioral and engagement data. We help structure that data for analytics and personalization. Your product team can use predictive analytics to act on what learners actually do, not just what they sign up for.

MarTech

Marketing stacks collect data from too many places. Often, attribution breaks and segments drift. We bring order to the pipeline from event tracking and identity resolution through to the clean datasets that feed campaign models and reporting.

Your industry

Our expertise runs deep in these four areas, but not exclusively. We work across industries where data scale, quality, or compliance is a real constraint. If your sector isn't listed, let’s discuss your needs.

Data Engineering Services for Healthcare

As an experienced data engineering company, we've built data infrastructure for HealthTech startups and established healthcare organizations, and we understand how those constraints shape every technical decision.

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Why TechMagic

End-to-end data engineering services

As a data engineering company, we offer end-to-end data engineering services to clients from different industries. Our services cover the entire data lifecycle, from the discovery phase through implementation and maintenance services. Our data engineering experts have experience working with various data architectures and platforms, such as AWS, Azure, etc.

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Compliance

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Experienced specialists

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