DATA ENGINEERING SERVICES
Data engineering services for reliable, AI-ready data
Build reliable data infrastructure that supports analytics, business operations, and AI.
Modus Create designs, builds, and improves data platforms that connect disparate systems, automate data workflows, and make information more reliable and accessible. From data architecture and pipeline engineering to governance, quality, and observability, we help engineering teams turn complex data environments into infrastructure they can operate, scale, and trust.
Data engineering challenges we help solve
Data challenges often start small. Over time, they can slow down teams, increase costs, and make it harder to use data across the business.
Fragmented data across systems
Data spread across disconnected applications, databases, and cloud platforms makes it difficult to get a consistent view of the business. Teams need reliable ways to connect these sources and keep data consistent.
Unreliable data pipelines
Failed jobs, manual workarounds, and pipelines that are difficult to maintain can delay data delivery and disrupt downstream processes. Better testing, monitoring, and error handling help teams keep data flowing reliably.
Inconsistent data quality
Missing values, duplicate records, and conflicting definitions undermine trust in reports and applications. Data validation and clear ownership help teams identify issues and address them at the source.
Data platforms that struggle to scale
As data volumes and workloads grow, existing platforms may struggle to keep up. Slow queries, rising infrastructure costs, and limited flexibility can prevent teams from supporting new business needs.
Legacy data infrastructure
Outdated data warehouses, tightly coupled systems, and aging pipelines can make changes costly and risky. Modernizing these environments starts with understanding existing dependencies and deciding what to retain, replace, or rebuild.
Data that isn't ready for AI
AI applications depend on accessible, reliable, and well-governed data. Poor data quality, disconnected sources, and limited traceability can make it difficult to move from experimentation to production.
The engineering behind reliable data
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.
Data foundations and governance
Build a data foundation that teams can trust and maintain. We define how data is owned, managed, accessed, and governed across systems, with practices adapted to your technical environment and regulatory requirements.
Includes:
- Data governance strategy and operating models
- Metadata management and data cataloging
- Data ownership and stewardship
- Access controls and data security
- Data governance for regulated environments, including GxP, HIPAA, GDPR, and SOX, where applicable
Benefits:
- Consistent data definitions across teams
- Clearer ownership and traceability
- Better visibility into data access and compliance requirements
Pipeline architecture and orchestration
Build and maintain data pipelines that move and transform data reliably across systems. We design ETL/ELT workflows and streaming architectures around your data sources, processing needs, and latency requirements.
Includes:
- ETL/ELT pipeline design, automation, and testing
- Batch processing, real-time streaming, and event-driven pipelines
- Data validation and pipeline reliability checks
- Monitoring, alerting, and performance optimization
Benefits:
- More reliable data delivery
- Less manual pipeline maintenance
- Better visibility into pipeline failures and performance
Lakes and warehouses (cloud-native)
Design and improve data platforms for analytical and operational workloads. We help teams choose and evolve architectures across AWS, Azure, and Google Cloud, taking performance, scalability, governance, and cost into account.
Includes:
- Cloud data platform migrations and upgrades
- Data warehouse and lakehouse architecture
- Query performance and storage optimization
- Integration of data from multiple sources
Benefits:
- Easier access to data across systems
- Better query performance for analytical workloads
- More efficient use of storage and compute resources
- Governance aligned with the platform architecture
Data quality and observability
Understand the state of your data and identify issues before they affect downstream systems. We help teams establish data quality checks and monitoring practices that make pipeline behavior, data freshness, and failures easier to investigate.
Includes:
- Data quality assessments and validation rules
- Schema change detection and data lineage
- Pipeline monitoring and alerting
- Incident management and reliability practices
Benefits:
- Earlier detection of data issues
- Less time spent investigating pipeline failures
- Better visibility into data freshness and reliability
Is your data infrastructure ready for what comes next?
Modernize legacy data systems, improve pipeline reliability, and evolve your cloud data platform to support new business and analytics needs.
OUR TECHNOLOGY
Data engineering technologies and platforms
Cloud Platforms
- AWS (S3, Glue, Lambda, Athena)
- Azure (Data Factory, Synapse, Data Lake)
- GCP (BigQuery, Dataflow, Cloud Storage)
Data Tools
- Apache Spark
- Kafka
- Airflow
- DBT
- Snowflake
- Databricks
Data engineering solutions by industry
Life sciences and pharma
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 services
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
Automotive
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
Retail
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
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.
OUR EXPERTS
Meet our data engineering experts
Our data engineering experts help organizations build reliable, scalable, and AI-ready data foundations. From modern data architectures and cloud platforms to governance and pipeline optimization, they turn complex data challenges into actionable business outcomes.
Fabio Oliveira
Principal, Data & Machine Learning Engineer
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.