Data Engineering Services
Data Engineering Services Designed for Business Impact
Your data should support your growth, not stand in its way. If your business is bogged down by fragmented information stored in disconnected systems, and manual work is slowing down critical operations, we know how to fix it.
STX Next provides data engineering services covering data lakehouse implementation, pipeline development, and analytics engineering for companies moving off legacy or fragmented data infrastructure. We partner with enterprise and mid-market companies in regulated industries to solve the challenges of siloed and unreliable data. This enables your teams to trust their reporting, scale their analytics, and move faster on AI initiatives.
We’ve successfully completed more than 100 data engineering projects. Our team of over 30 data engineers has worked in finance, manufacturing, insurance, and healthcare.
Our data engineering consulting services
Bad data infrastructure is a business problem, not just a technical one.
Delayed decisions, conflicting reports, AI projects that stall before launch are the symptoms of messy data. And as long as that foundation is broken, scaling your operations or adopting more advanced solutions stays out of reach.
Fix these problems at the source with a single, reliable platform where your data can be governed, trusted, and used easily.
1. Data Platform Architecture & Design
- Data lakehouse design and implementation (Snowflake, Databricks, Microsoft Fabric, AWS-native)
- Medallion architecture (Bronze / Silver / Gold layers) for structured data maturity
- Cloud-native data warehouse design on AWS, GCP, and Azure
- Architecture assessments, gap analysis, and target-state roadmaps
- PoC implementations to validate architecture before full commitment
Without a coherent architecture, organizations end up with a patchwork of disconnected tools. Engineering teams spend their time firefighting inconsistencies instead of delivering value, and business decisions get made on data nobody fully trusts.
A well-designed platform solves this at the foundation.
2. Data Ingestion & Pipeline Development
- Multi-source ingestion from REST APIs, SaaS platforms, ERP/CRM systems, and legacy databases
- Batch ETL and incremental/CDC (Change Data Capture) pipelines
- Real-time ingestion using Kafka, Kinesis, Azure Event Hub, and Snowpipe
- IoT telemetry ingestion from industrial sensors and devices
- File-based ingestion from SFTP, S3, SharePoint, and internal storage systems
- Web scraping pipelines for external data enrichment
Many organizations have valuable data locked in siloed systems, third-party APIs, legacy databases, or file exports that never reach analytics workflows. The result is reporting that is incomplete, stale, or manually assembled from spreadsheets.
Automated, well-designed ingestion pipelines remove this bottleneck.
3. Data Transformation & Modeling
- Semantic data modeling using dbt (documentation, quality gates, version control)
- Complex ETL orchestration with Apache Airflow and Azure Data Factory
- PySpark and Spark SQL transformations for high-volume datasets
- Cross-market data normalization and standardization
- Statistical and cross-tabulation processing for research datasets
Raw data is rarely usable as-is as different systems encode the same concept differently. Without a structured transformation layer, every team ends up maintaining their own version of the truth and analysts spend more time cleaning data than analyzing it.
Standardized modeling with dbt changes this: every metric is defined once, tested automatically, documented clearly, and versioned like code.
4. Real-Time Streaming & Event Processing
- High-throughput stream processing with Apache Kafka, Apache Flink, and Apache Beam
- Anomaly detection and alerting pipelines
- Real-time fraud detection systems
- IoT data stream processing: ingestion, aggregation, and alerting
- Event-driven warehousing pipelines for ML training and analytics
While batch processing is ideal for routine reporting, it falls short when immediate action is required – such as detecting a fraudulent transaction, responding to abnormal factory sensor data, or managing a sudden server failure. In these scenarios, every second of delay translates into measurable costs like financial loss, production downtime, or security breaches.
Real-time streaming infrastructure closes this gap by enabling immediate action.
5. Data Migration
- Migration from legacy monolithic systems to cloud-native data lakehouse
- Large-scale schema normalization across multiple fragmented databases
- Lift-and-shift plus re-architecture of existing data stacks
- Database replication and continuous transfer using AWS DMS
Legacy tech acts as a tax on every future data project. New analytics tools can't connect to them cleanly. Reporting is slow and unreliable. Maintenance drains budget and engineering resources that could be better spent elsewhere. As data volumes grow, these systems tend to degrade rather than scale.
Migrating to modern cloud-native infrastructure resolves these issues by replacing a fragmented legacy setup with a unified platform.
6. Data Quality, Observability & Governance
- Automated data quality validation using Great Expectations, dbt tests, and Soda SQL
- Data lineage tracking and metadata management (DataHub, Unity Catalog, Microsoft Purview)
- Data governance frameworks covering access control, documentation, and stewardship
- Pipeline health monitoring and alerting (Monte Carlo, Datadog, Grafana)
- GDPR and HIPAA-compliant data architecture design
Most organizations don't realize how much bad data is costing them until they try to use it for something that matters. Analysts spend hours reconciling conflicting numbers. Executives distrust dashboards. AI models trained on unvalidated data produce unreliable outputs. In regulated industries, inadequate governance is a compliance and reputational liability, not just a technical inconvenience.
Embedding quality and governance into the platform from the start addresses these problems by establishing a structured normalization process and proper access controls.
7. Analytics Engineering & BI
- Unified metrics frameworks and semantic layers for consistent reporting
- Dashboard consolidation and BI rationalization (Tableau to Hex, Power BI migration)
- Self-service reporting platforms with row-level security
- Cross-channel marketing attribution modeling
- Operational and financial dashboards for business stakeholders
- Real-time dashboards for monitoring production, server fleets, and ad performance
Most organizations have too many, with too little clarity. Redundant reports built by different teams using different definitions create confusion rather than alignment. Decision-makers end up asking "which number is right?" instead of acting on the data.
Analytics engineering addresses this by treating reporting as a product, directly accelerating research workflows and supporting better-informed decisions across the organization.
8. AI/ML Data Infrastructure
- ML-ready data pipelines for feature engineering, model training, and retraining
- Semantic and vector search infrastructure using ElasticSearch, BigTable, and FAISS
- Embedding generation pipelines with real-time indexing via Pub/Sub
- Credit scoring and predictive model data pipelines
- Predictive health analytics pipelines
- Sentiment analysis integration from call center data
- Product similarity and matching at billion-record scale
AI projects frequently fail not because the models are wrong, but because the data feeding them is unreliable and poorly structured. Feature engineering pipelines built on inconsistent data produce models that don't generalize. Training data with quality issues creates systems that are confidently wrong.
Building proper ML data infrastructure changes the equation, providing reliable data foundations.
9. Data Integration & Systems Connectivity
- CRM, ERP, and SaaS tool integration (Salesforce, MS Dynamics, HubSpot, Shopify)
- API microservice development for internal data routing and delivery
- Multi-warehouse consolidation and cross-system data reconciliation
- Custom ETL proxies replacing paid third-party tools (Marketplace Tech)
- Event technology and payment platform API integration
Without integration, each software tool becomes a data silo. Marketing doesn't see what sales knows. Finance can't reconcile what operations reports. Customer-facing teams make decisions without a complete picture of behavior across channels.
Proper systems connectivity eliminates these gaps by unifying data across multiple tools into a single source of truth.
10. DataOps, Infrastructure & DevOps
- Infrastructure as Code using Terraform and Kubernetes
- CI/CD pipelines for data workflows (GitHub Actions, GitLab CI, Azure DevOps)
- Containerized pipeline deployment with Docker and ECS/Fargate
- Centralized code repositories and orchestration setup for previously ad-hoc scripts
- Monitoring, alerting, and automated remediation for server fleets
Data pipelines built without proper engineering practices are fragile. When infrastructure is managed through ad-hoc scripts with no version control, the entire data operation becomes dependent on institutional knowledge held by a small number of people.
Applying software engineering discipline to data infrastructure changes this durability profile entirely by replacing fragmented, ad-hoc scripts with standardized, version-controlled pipelines.
11. Data Strategy & Consulting
- Data needs assessments and target architecture blueprints
- Technology benchmarking and tool selection advisory
- Data platform maturity scoring and improvement roadmaps
- Governance posture reviews and lightweight governance layer implementation
- Team training and bootcamps to build internal data capability
Investing in the wrong tool, building a platform that doesn't fit the team's actual workflows, or scaling a flawed architecture all carry costs that are hard to reverse.
Strategy work done upfront prevents this. And we have quite a bit of experience in this.
Expertise built on +100 data engineering projects
Partnering with us, our clients have cut incident response times from days to minutes, consolidated thousands of redundant dashboards into focused reporting, and built systems that could never have run on their previous infrastructure.
Real-time IoT data platform replacing legacy ETL for high-volume factory telemetry
A global chemical company needed to process roughly 100 million telemetry records per day across 11 factories, but their existing ETL tooling couldn't handle the scale or deliver timely insights. We built a streaming data pipeline on Azure Event Hub feeding directly into Azure Data Explorer, where in-stream aggregation and transformation happen at the source. Python-based microservices handle targeted data access and custom analytics, with results exposed to Power BI for live factory KPIs. The result: real-time visibility into production metrics, eliminated third-party ETL costs, and a pipeline architecture built to scale with new data sources.
US
Research data platform replacing legacy analytics for global market intelligence
One of the biggest global automotive enterprises struggled to consolidate and analyze years of market research data because of a costly and inflexible legacy system. Our team built a custom data platform on Azure that automates ingestion and normalization from SPSS files and online forms, ensuring consistency across markets.
Germany
Unified EdTech platform modernizing content delivery across global learning products
Macmillan needed to consolidate multiple digital learning tools into a single platform that could scale across regions and improve user experience. STX Next provided the backend services, data pipelines, and CI/CD infrastructure underpinning the Macmillan Education Everywhere platform, alongside 30+ interactive tools. Deep integrations with Google Classroom, AWS, and Elasticsearch keep content delivery fast and consistent.
UK
Modern Data Lakehouse for scalable, trusted data
At STX Next, the data lakehouse is our primary architectural approach as it combines the flexibility of a data lake with the performance and reliability of a data warehouse.
Why choose a lakehouse
One unified data platform
for BI, analytics, and AI
Scalable and cost-efficient architecture
that grows with your needs
Built-in governance, lineage, and quality controls
for reliable reporting
Faster time-to-value
with a future-ready foundation for innovation
Partnering with us, you get 20 years of engineering experience with deep expertise in Snowflake, Databricks, and Apache Iceberg. We build platforms your teams want to use, delivering trusted data, clear business value, and the flexibility to scale without adding technical debt.
Data engineering solutions built for your industry
At STX Next, we don't believe in one-size-fits-all solutions – we partner with you to create data systems that align with your business reality and drive measurable results.
Finance
Financial services run on real-time, secure data. We help fintechs and institutions manage complex pipelines, meet regulatory demands, and deliver insights fast.
Real-time fraud detection
Leverage streaming data pipelines to detect suspicious activity as it happens, minimizing losses and protecting users before a transaction completes.
Customer segmentation and scoring
Build unified data models that support precise risk assessments and enable hyper-personalized financial products at scale.
Regulatory reporting automation
Automate compliance workflows with accurate, continuously updated pipelines aligned with standards like PSD2, AML, and SEC guidelines.
See our finance solutions
Oil & Gas
Energy companies deal with high-volume sensor data, complex infrastructure, and tightening efficiency requirements. We build platforms that turn operational data into clear, actionable intelligence across the entire value chain.
IoT and SCADA data integration
Ingest telemetry from meters, turbines, pipelines, and field sensors into a unified platform for real-time monitoring and historical analysis.
Predictive maintenance pipelines
Use streaming data and ML models to detect equipment anomalies early, reducing unplanned downtime and extending asset lifespans.
Energy consumption and cost optimization
Track usage patterns across facilities and identify inefficiencies automatically, giving operations teams the data they need to reduce waste and control costs.
See our energy solutions
Industrials
Industrial operations often run on systems that were never designed to talk to each other – legacy SCADA, on-premises ERP, modern IoT sensors, and cloud analytics sitting in separate silos. Bringing them together without disrupting operations requires a migration approach that respects what's already working while building toward future readiness.
Unified operational data platform
Consolidate data from ERP systems, CRM tools, spreadsheets, and field sources into a single lakehouse that serves both operational and analytical needs.
Real-time KPI dashboards
Deliver live visibility into inventory, logistics, warehouse performance, and financial metrics, so teams can act on current data rather than yesterday's reports.
Forecasting and demand planning
Implement data models that combine historical trends and real-time signals to support more accurate planning across sales, procurement, and distribution.
See our industrials solutions
Manufacturing
Production environments generate continuous streams of data that most organizations never fully use. We help manufacturers capture, process, and act on that data to improve efficiency and reduce failures.
IoT data stream processing
Capture telemetry from industrial sensors – temperature, pressure, speed, ink levels, and more – and convert raw signals into insights that operators and engineers can act on in real time.
Supply chain forecasting
Enable accurate demand planning and inventory distribution based on real-time data and historical production trends, reducing both overstock and shortfalls.
Quality and process monitoring
Build pipelines that track production metrics continuously, flagging anomalies and deviations before they become costly defects or line stoppages.
See our manufacturing solutions
Healthcare
Data in healthcare must be secure, accurate, and interoperable. We help healthcare companies consolidate fragmented clinical and operational data while staying compliant with strict regulatory requirements.
Medical data integration
Consolidate data from EMRs, labs, wearables, and third-party platforms into a single source of truth that supports better patient care and more reliable clinical reporting.
Predictive health analytics
Enable earlier intervention with ML-powered pipelines that detect patient deterioration risks and surface patterns across large clinical datasets in real time.
Compliance-focused data architecture
Design governance frameworks and secure access controls that meet HIPAA and GDPR requirements from day one, so compliance is built into the platform rather than added later.
AdTech / MarTech
Marketing and advertising teams produce enormous volumes of event data across multiple platforms. We build the infrastructure that turns that data into reliable attribution, sharper targeting, and faster campaign decisions.
Cross-channel attribution modeling
Unify event data from ad platforms, web analytics, and CRM systems to build accurate attribution models that show which channels and campaigns are genuinely driving results.
Real-time campaign performance pipelines
Ingest and process ad performance data from Google, Meta, and other platforms continuously, so teams can adjust campaigns while they're still running rather than in the next reporting cycle.
Audience segmentation and targeting infrastructure
Build data models that combine behavioral, transactional, and demographic signals to power precise audience targeting and personalized content delivery at scale.
See our AdTech / MarTech solutions
How we work
At STX Next, we combine Agile flexibility with engineering principles, ensuring transparency and collaboration throughout the process.
Tech expertise
Snowflake, Redshift, BigQuery, Databricks, Kafka, Airflow, PostgreSQL, TimescaleDB
Apache Kafka, Flink, Kinesis, OpenTelemetry, Apache Beam
Great Expectations, Monte Carlo, Tableau, Superset, Datafold, Soda SQL
AWS, GCP, Azure, Kubernetes, Terraform, dbt Cloud, Looker, Prometheus, Grafana
TensorFlow, PyTorch, scikit-learn, pandas, NumPy, Spark, Airflow, ML Ops platforms
REST, GraphQL, gRPC, RabbitMQ, Kafka
Jenkins, GitLab CI, GitHub Actions, Prometheus, Grafana, ELK Stack, Cypress, Selenium, Pytest, SonarQube
Why STX Next
Over 20 Years of Engineering Experience
STX Next combines production-grade software delivery with a mature, strategic data practice. Our approach blends cross-domain experts, with proven governance processes, and powerful tooling. Every solution we deliver is not only technically sound but also maintainable, scalable, and aligned with your business reality.
Prime Integrator for modern lakehouses
We design and implement lakehouse architectures on Snowflake and Databricks using open technologies like Apache Iceberg. The priority is always selecting the right fit for your specific ecosystem rather than pushing a default stack.
Multi-Source Data Ingestion, Cleaning & Wrangling
Our data ingestion practice connects data from all corners of your organization, from legacy systems to event streams, into a clean, analysis-ready foundation built around your business logic. We engineer ingestion flows that are resilient, scalable, and cost-controlled, using cloud-native tooling that fits your existing stack.
Standardized Data Modeling & Assurance Practices
Using a standard development framework across the platform ensures every data product ships with semantic modeling, built-in quality checks, clear documentation, and consistent metric definitions. The result is a data layer that both technical and non-technical teams can trust and act on.
Embedded Data Catalog & Governance
Governance is built into every platform we deliver, covering lineage, metadata, access controls, and shared definitions as standard. Our clients consistently point to this as what makes both decision-making and AI adoption much more efficient.
Training & Bootcamps
To accelerate adoption and build internal confidence, we offer dedicated bootcamps for engineering, analytics, and business teams. These programs transfer practical knowledge, demystify the platform, and ensure teams feel ownership of the solution.
Business-Ready AI-Powered Analytics
By combining data lakehouses with intelligent analytics – from RAG-based extraction to predictive modeling – dashboards are built around real decisions rather than vanity metrics. Narrative-driven layouts and problem-oriented storytelling guide action and accelerate interpretation, grounding every decision in usable data insight.
You earn the trust of your team through the miles that you run together. Over the years, we ran a number of marathons together with STX […] working and sweating side by side. That creates a strong sense of team – not only within STX, but also in the cooperation between Decernis and STX. I really don’t have doubts that if I need something […] they’re there to work with me.
Don’t just take our word for it:
Meet your data engineering experts
Get ready to meet the talented individuals who make it all happen. Our team isn't just a group of skilled engineers – they're the people who turn your biggest challenges into great solutions.
An experienced data engineering leader focused on building cloud-native platforms that combine performance, cost efficiency, and quality assurance. He supports business and technology leaders in maximizing the impact of their data initiatives through tailored solutions and strong team collaboration.
Let's talk
Schedule a chat with Tomasz and one of our senior engineers to discuss your data engineering needs.
FAQ
When should a company invest in professional data engineering services?
You should consider external expertise when your internal team spends more time firefighting data infrastructure than delivering strategic insights. Critical technical and business signals include:
- Unreliable pipelines that cause frequent reporting delays or dashboard downtime.
- Fragmented data sources locked in disconnected SaaS, ERP, or legacy systems.
- Poor data quality leading to conflicting metrics and executive distrust in analytics.
- AI readiness issues, where machine learning or GenAI projects stall because the underlying training data is unvalidated or unstructured.
If scaling your operations or adopting advanced analytics is blocked by your foundational infrastructure, specialized data engineering services will fix these issues at the source.
What does the timeline and risk-mitigation process look like for a platform overhaul?
To eliminate financial and technical risk, we break projects down into distinct, low-risk phases:
- Discovery Phase: We assess your current data maturity, map out target-state roadmaps, and align architecture with your business goals.
- Proof of Concept (PoC): Before you commit your full budget, we build a working prototype to validate the architecture, tech stack, and data viability.
- Iterative Sprints: We deliver functional components using Agile methodologies, ensuring you see continuous progress and maintain full visibility into the timeline.
How do you handle technical complexity and integration with our existing tools?
We do not believe in forcing a proprietary stack or a one-size-fits-all tool onto your business. Our team specializes in designing flexible, modern data lakehouses using industry leaders like Snowflake, Databricks, and Apache Iceberg, as well as cloud-native setups on AWS, GCP, and Azure.
Whether your data lives in legacy monolithic databases or modern SaaS APIs, we engineer custom ETL/ELT pipelines and semantic layers (using tools like dbt and Apache Airflow) that seamlessly connect your entire ecosystem without forcing you to abandon your current investments.
How are data engineering services priced, and how do you ensure a predictable ROI?
The cost depends entirely on the scale, data volume, and structural complexity of your environment. To ensure budget predictability and minimize upfront risk, we typically begin with a scoped architecture assessment or a targeted PoC.
Investing in expert data engineering services upfront prevents the massive, hard-to-reverse costs of building on a flawed architecture or choosing the wrong enterprise licensing down the line. Our clients see immediate ROI through eliminated third-party ETL vendor costs, drastically reduced cloud compute waste, and the automated consolidation of redundant systems.
How do your data engineers integrate with our internal team and existing capacity?
We treat team fit and knowledge transfer as core deliverables. Our 30+ battle-tested data engineers apply rigorous software engineering discipline (DataOps) to your data environment.
We don't build a black box that leaves your team dependent on us. Instead, we implement Infrastructure as Code (Terraform), centralized repositories, and automated documentation. To wrap up our engagement, we host dedicated bootcamps and training sessions to guarantee your internal engineering, analytics, and business teams feel complete ownership of the new platform.
What migration disruptions should we expect, and how do you minimize them?
Migrating from legacy systems to a cloud-native lakehouse carries inherent operational risks, which we mitigate through a meticulous replication and validation strategy.
Using tools like AWS DMS and change data capture (CDC) pipelines, we run continuous data transfers in parallel with your live operations. We embed automated data quality testing (via Great Expectations and Soda SQL) to ensure zero data loss or schema mismatches, executing the final cutover only when the new system is fully validated. Your day-to-day operations experience minimal to zero downtime.
How does your data architecture address security, compliance, and governance?
Data governance is a fundamental architectural requirement, not an afterthought. We design frameworks that protect your business from reputational and compliance liabilities by building:
- Role-Based Access Control (RBAC): Row- and column-level security so users only see the data they are cleared to see.
- Automated Data Lineage: Full tracking (via Unity Catalog, DataHub, or Microsoft Purview) so you can audit exactly where a data point originated and how it was transformed.
- Regulatory Compliance: Architecture engineered from day one to meet strict regulatory standards like GDPR and HIPAA, ensuring sensitive data is handled securely throughout its entire lifecycle.
