DATA ENGINEERING
Data engineering solutions built for AI-ready infrastructure
Great products and decisions run on trustworthy data. Most teams wrestle with brittle pipelines, conflicting definitions, and slow time to insight. Modus Create designs, builds, and operates modern data engineering solutions that turn messy, siloed data into reliable, governed, and observable assets, ready for analytics, operations, and AI. From data modernization services to cloud data warehouses and real-time pipelines, we fix the foundations so your teams can ship with confidence.
From raw data to AI-ready infrastructure
Most data problems are not tool problems. They are architecture and process problems. Organizations invest in best-in-class analytics and AI tools, only to find that the data feeding those tools is incomplete, inconsistent, or unreliable. Modus Create closes the gap between raw data and trusted, governed, AI-ready infrastructure.
Every analytics initiative, AI project, and business decision runs on foundations built in this layer. We design and implement governance frameworks that define ownership, enforce quality, and ensure compliance, so every team works from the same trusted source.
Includes:
- Data governance strategy and operating model
- Metadata management and data cataloging
- Data ownership and stewardship frameworks
- Access control and data security policies
- Compliance readiness for GxP, HIPAA, GDPR, and SOX
Benefits:
- A single source of truth across every team and system
- Reduced compliance risk and audit-ready documentation
- Faster onboarding for new data consumers and analysts
Move and transform data with speed and reliability. Pipelines are designed and built for scale, integrated with your existing cloud infrastructure.
Includes:
- ELT/ETL automation and testing
- Real-time streaming and event-driven pipelines
- Data validation, quality checks, and SLAs
- Monitoring, alerting, and cost controls
Benefits:
- Faster time to insight
- Higher data accuracy
- Reduced manual operations
Centralize and optimize for analytical and operational workloads. Architectures are designed for performance, cost efficiency, and governance across AWS, Azure, and GCP.
Services:
- Cloud migrations and platform upgrades
- Performance tuning and cost optimization
- Multi-source integration at scale
Benefits:
- Unified access across teams and tools
- Better analytics with optimized query performance
- Efficient storage at any data volume
- Strong governance built into the architecture
Always know the state of your data. Quality frameworks and observability layers are put in place so every dataset is trustworthy, every pipeline is visible, and every anomaly is caught before it reaches production.
Services:
- Quality assessments and scorecards
- Schema change detection and lineage
- Incident management and reliability playbooks
Benefits:
- Fewer pipeline breakages
- Confident decisions based on verified data
- Measurable uptime and SLA adherence
OUR TECHNOLOGY
Data engineering technologies and platforms
- AWS (S3, Glue, Lambda, Athena)
- Azure (Data Factory, Synapse, Data Lake)
- GCP (BigQuery, Dataflow, Cloud Storage)
- Apache Spark
- Kafka
- Airflow
- DBT
- Snowflake
- Databricks
Data engineering solutions by industry
For pharma, biotech, and CRO organizations, data infrastructure is a regulatory requirement as much as a technical one. Our work in this space covers GxP-compliant data platforms, from genomics data pipelines to real-world evidence platforms and clinical trial data infrastructure.
GxP-compliant data governance and audit-ready infrastructure, data platforms supporting regulatory and clinical workflows, Cloud-native infrastructure modernization for life sciences organizations, AI-ready data foundations for pharma and biotech teams
Financial data demands real-time accuracy, strict access controls, and audit-ready governance. Our engagements in this sector cover infrastructure that meets regulatory requirements without sacrificing the speed teams need to operate.
Real-time analytics pipelines, regulatory reporting frameworks, customer 360 platforms, fraud detection data infrastructure
Connected vehicles generate large volumes of data. For automotive clients, we build the cloud data infrastructure that turns telematics, sensor data, and supply chain signals into actionable insights.
Cloud data infrastructure for connected vehicle platforms, data pipelines supporting software-defined vehicle development, data platform modernization for automotive organizations
High volume, high velocity, high stakes. Retail data infrastructure is built to power personalization, demand forecasting, and omnichannel analytics at the scale today's retail organizations require.
Omnichannel data platforms, personalization and customer analytics infrastructure, integration across digital and physical retail systems
Proof of work
Data engineering case studies and proof of work
ENERGY & MARINE ANALYTICS
Custom data pipelines for offshore wind and commercial fishing analytics
Last Tow, a marine consultancy firm, needed to analyze surf clam fishing activity in waters leased for offshore wind development. Modus Create engineered a custom data pipeline that ingested Vessel Monitoring System (VMS) data, unified scattered geospatial sources from four fishery companies, and built visualizations that informed mitigation strategies between renewable energy developers and the fishing industry.
- 10,000+ miles of vessel activity ingested, cleaned, and analyzed with PySpark
- Standardized geospatial datasets across four fishery exports using ogr2ogr, GeoPandas, and GeoPy
- Reusable framework for marine analytics, documented in a shared GitHub repo with CI/CD
LIFE SCIENCES
ML-powered cancer care platform built on AWS with real-time data pipelines
A global biopharmaceutical leader operating in 125+ countries partnered with Modus Create to build a real-time cancer care platform powered by wearable sensors and patient data. We engineered a HIPAA-compliant, FDA-validated AWS architecture combining IoT data ingestion, ML-based anomaly detection on Amazon SageMaker, and encrypted patient data storage on Amazon RDS.
- 42% increase in patient engagement through continuous monitoring
- 94% faster clinical decision-making, from days to minutes
- 27% reduction in unplanned hospital visits via early symptom detection
USE CASES
Common data engineering challenges we solve
Bad data propagates downstream before anyone notices. By the time it surfaces in a dashboard or a model output, the damage is done and the root cause is hard to trace.
When pipelines are fragile, data teams spend most of their time firefighting. The work that actually moves the business forward keeps getting pushed.
Finance, marketing, and operations are all pulling from different systems and getting different answers. Decisions slow down. Trust in data erodes.
Legacy data warehouse systems were built for a different scale and a different pace. They create bottlenecks that block new use cases, new teams, and new data sources.
Most AI projects do not fail because of the model. They fail because the data feeding the model is incomplete, ungoverned, or inconsistently delivered.
When auditors ask where a number came from, the answer needs to be immediate and documented. Missing lineage and weak access controls turn routine audits into fire drills.
Why teams choose Modus Create for data engineering
We're not a pure-play data shop. Our data engineering services sit alongside AI/ML, platform engineering, and product engineering teams, so the infrastructure we build is connected to how your products and AI workloads actually run.
AI-ready from day one
Every architecture decision accounts for downstream AI and analytics workloads, not retrofitted later.
Regulated-industry depth
GxP, HIPAA, GDPR, and SOX compliance built into governance frameworks, with proven track record in life sciences and financial services.
Cloud-native and cloud-agnostic
Certified on AWS, Azure, and GCP. We design for portability and cost efficiency, not vendor lock-in.
Engineering, not just advisory
We build and operate what we recommend. Our teams stay through to production hardening.
Our partners
Technology partners supporting data engineering
Our cloud and data partnerships give clients access to certified expertise across the full data engineering stack, from ingestion and storage to governance and AI readiness. AWS, Google Cloud, and Azure certifications mean architectures are designed with native services in mind, not bolted on. Our InfluxData partnership extends our observability and time-series capabilities for clients dealing with high-frequency operational data.
INSIGHTS
Data engineering insights and research
"A lot of startups would benefit from the experience Modus Create brought to the table. It has set a very solid foundation on which we can grow now."
"From a business value point of view, the AVP ensures cost savings of about 30%, and the time to market has been reduced from weeks to hours."
"Modus Create understood our challenges and was committed to provide a solution that met our goals."
"Modus Create was an amazing partner for our GitHub migration to the GitHub cloud. We needed someone that could come in with expertise and show us the way, not just supplement our capacity."
LET'S GET STARTED
Talk to Modus Create
Big challenges need bold partners. Let’s talk about where you want to go — and start building the path to get there.
Data engineering: Frequently Asked Questions
What are data engineering solutions?
Data engineering solutions cover the design, build, and operation of the infrastructure that moves, transforms, stores, and governs data across an organization. This includes data pipelines, data warehouses, data lakes, governance frameworks, and observability tools. Our engagements are tailored to each organization's architecture, compliance requirements, and business objectives rather than applied from a generic template.
What data modernization services does Modus Create offer?
Our data modernization services help organizations move from legacy data systems to modern, cloud-native architectures. That includes migrating from on-premise data warehouses to Snowflake, Databricks, or BigQuery; redesigning brittle ETL pipelines into resilient orchestrated workflows; and implementing governance and observability layers on top of existing infrastructure. Engagements begin with a current-state assessment and produce a prioritized modernization roadmap.
What is the difference between a data lake and a data warehouse?
A data warehouse stores structured, processed data optimized for analytics and reporting. A data lake stores raw data in its native format, including structured, semi-structured, and unstructured data, at lower cost and higher volume. Modern lakehouse architectures combine the benefits of both, enabling flexible storage alongside structured query performance. The right architecture depends on your specific use case, data volumes, and downstream consumers.
Does Modus Create build data infrastructure for regulated industries?
Yes. A significant share of our data engineering work is in regulated environments including life sciences, pharma, and financial services. Every data platform built in a regulated context includes governance frameworks, access controls, data lineage, and audit trails that meet GxP, HIPAA, GDPR, and SOX requirements. Life sciences clients include pharma, biotech, and CRO organizations requiring compliant data infrastructure to support clinical and regulatory workflows. See how this work applies in practice.
How does data engineering support AI and machine learning?
AI and machine learning models depend on clean, governed, and consistently delivered data to produce reliable outputs. The infrastructure we build makes AI initiatives viable: observable pipelines that feed models on a defined schedule, feature stores that make ML features reusable across teams, and MLOps data infrastructure that supports model training, validation, and deployment at scale. Poor data foundations are the most common reason AI projects stall or fail in production.
What does a data engineering consulting engagement look like?
Engagements start with an assessment of current data infrastructure, identifying bottlenecks, governance gaps, and architectural debt. From there, we produce a prioritized roadmap and build the systems that close those gaps. Projects range from targeted pipeline redesigns to full data platform modernization programs, with embedded engineers working alongside client teams throughout.
What is data observability and why does it matter?
Data observability is the ability to understand the health and state of data across pipelines, warehouses, and systems at any point in time. It covers monitoring for schema changes, data quality anomalies, pipeline failures, and SLA breaches. Without it, data issues propagate silently and reach end users or AI models before anyone detects a problem.