We design, build, and operate data engineering and data science systems that turn raw operational data into reliable pipelines, governed warehouses, and decision-grade models. From ingestion and orchestration to MLOps and analytics, we cover the full lifecycle so your teams can ship faster and trust the numbers behind every dashboard, forecast, and product feature.
- End-to-end ownership: ingestion → warehouse → modeling → ML → serving
- Cloud-native stacks on AWS, GCP, and Azure with open-source cores
- Production MLOps: reproducible training, model registry, drift monitoring
- Security and governance baked in — not bolted on
Data is now the single most strategic raw material a modern organization can own. Yet most enterprises sit on vast reserves they cannot refine: siloed sources, brittle spreadsheets, duplicated dashboards, and models that never reach production. The gap between having data and acting on data is where competitive advantage is won or lost.
Our data engineering and data science practice closes that gap. We treat data as a product: versioned, tested, documented, and served with the same rigor as application code. Whether you need a greenfield lakehouse, a rescue of a stalled analytics program, or a production machine learning capability, we bring the architecture, engineering, and science to make it real — and keep it running.
Data engineering is the load-bearing wall of any analytics or ML program. If ingestion is unreliable, the warehouse is stale. If the warehouse is stale, every dashboard lies. We build the infrastructure that makes data trustworthy by default — not by accident.
Pipeline Architecture & Ingestion
Batch and streaming pipelines that ingest from databases, APIs, event buses, files, and SaaS platforms. We standardize on idempotent, backfill-friendly designs so reprocessing never corrupts downstream tables.
- Change Data Capture (CDC) for low-latency replication
- Schema evolution and contract enforcement
- Exactly-once semantics where business logic demands it
- Connector frameworks for 200+ source systems
Warehouse & Lakehouse Design
Cloud data warehouses and lakehouses modeled for performance, cost, and governance. We implement medallion architectures (bronze, silver, gold) with clear separation between raw, conformed, and curated layers.
- Star and Data Vault modeling for analytics readiness
- Columnar storage tuning for sub-second queries
- Cost controls: auto-suspend, clustering, materialized views
- Unity Catalog, Lake Formation, and Purview integration
Orchestration & Scheduling
Workflow orchestration that makes dependencies, retries, and SLAs explicit. We favor declarative DAGs so pipelines are version-controlled, testable, and reviewable like application code.
- Airflow, Dagster, and Prefect implementations
- SLA monitoring and smart retry with exponential backoff
- Cross-environment promotion: dev → staging → prod
- Lineage tracking end-to-end from source to dashboard
Data Quality & Observability
Automated validation that catches broken data before it reaches decision-makers. Great expectations, dbt tests, and custom assertions embedded directly into pipeline runs.
- Freshness, volume, schema, and distribution checks
- Anomaly detection on key business metrics
- Incident routing to Slack, PagerDuty, and email
- Data contracts between producers and consumers
Streaming & Real-Time Analytics
Event-driven architectures for sub-second decisioning. Kafka, Kinesis, and Pulsar backbones with stream processing for fraud detection, personalization, and operational alerts.
- Stream processing with Flink, Spark Structured Streaming
- Materialized views and real-time serving layers
- Exactly-once and event-time windowing
- Change feeds synced to operational databases
Governance, Security & Privacy
Role-based access, column masking, audit trails, and lineage that satisfy SOC 2, HIPAA, and GDPR. Governance is designed in from day one, not retrofitted after an audit fails.
- Attribute-based access control (ABAC) policies
- PII discovery, tagging, and dynamic masking
- End-to-end lineage for impact analysis
- Audit logging and compliance reporting
Engineering Deliverables Comparison
| Deliverable | What You Get | Typical Timeline | Best For |
|---|---|---|---|
| Pipeline Audit | Full codebase review, bottleneck map, remediation roadmap | 2–3 weeks | Teams with existing but unreliable pipelines |
| Warehouse Modernization | Re-architected warehouse, migrated workloads, performance baseline | 6–12 weeks | Legacy on-prem or underperforming cloud warehouses |
| Greenfield Platform | End-to-end lakehouse, CI/CD, catalog, and first production pipelines | 10–16 weeks | Organizations building their first modern data stack |
| Real-Time Streaming Layer | Event backbone, stream processors, serving API | 4–8 weeks | Use cases needing sub-second latency |
| Managed Operations | 24/7 monitoring, on-call, incident response, continuous optimization | Ongoing | Teams without dedicated data platform SREs |
Data science only creates value when models reach production and stay there. We focus on the full arc — from problem framing and experimentation to deployed models with monitoring, drift detection, and rollback. No science projects that die in a notebook.
Exploratory Analysis & Hypothesis Testing
We start with the business question, not the algorithm. Rigorous EDA, statistical testing, and experiment design separate signal from noise before a single model is trained.
- Cohort, funnel, and retention analysis
- A/B and multivariate test design with power analysis
- Causal inference for decision impact
- Feature ideation grounded in domain context
Predictive Modeling & Machine Learning
Classification, regression, forecasting, and recommendation systems built with reproducible workflows. Model selection driven by business metrics, not just leaderboard scores.
- Gradient boosting, deep learning, and ensemble methods
- Time-series forecasting (Prophet, neural, hierarchical)
- Recommendation engines and ranking models
- Hyperparameter optimization at scale
NLP & Large Language Models
Text intelligence and LLM-powered features with retrieval-augmented generation, fine-tuning, and evaluation harnesses. We build guardrails, eval suites, and cost controls so generative features are safe to ship.
- RAG pipelines with vector search and reranking
- Fine-tuning and LoRA adaptation for domain specificity
- LLM evaluation: faithfulness, relevance, toxicity
- Prompt management and version control
Computer Vision
Image and video intelligence for quality control, document processing, and visual search. Edge deployment options for latency-sensitive or air-gapped environments.
- Object detection, segmentation, and classification
- OCR and document understanding pipelines
- Model distillation for edge and mobile
- Active learning loops with human-in-the-loop review
MLOps & Model Lifecycle
Production-grade ML operations: reproducible training, model registry, automated deployment, and continuous monitoring. Models that degrade are caught and rolled back automatically.
- Feature stores for training-serving consistency
- Model registry with stage transitions and approvals
- Shadow and canary deployments
- Drift detection and performance decay alerts
Analytics & Decision Intelligence
Beyond models: we build the decision layer that translates predictions into actions. Prescriptive analytics, optimization, and simulation that close the loop between insight and outcome.
- Self-serve analytics platforms and semantic layers
- Optimization and operations research models
- Scenario planning and Monte Carlo simulation
- Decision APIs embedded in business workflows
We are cloud-native and tool-agnostic. Our default stack favors open-source cores with managed cloud services for operational simplicity, but we adapt to your existing investments — we never rip and replace for the sake of novelty.
| Layer | Primary Tools | What We Evaluate On |
|---|---|---|
| Ingestion | Airbyte, Fivetran, Debezium, Kafka Connect, custom connectors | Reliability, schema evolution, backfill support, CDC latency |
| Storage | Snowflake, BigQuery, Databricks, Redshift, Postgres, Iceberg | Query performance, cost per TB scanned, concurrency, governance |
| Processing | Spark, dbt, Flink, Dataflow, Ray, Polars | Throughput, developer ergonomics, streaming semantics |
| Orchestration | Airflow, Dagster, Prefect, Step Functions | DAG-as-code, observability, backfill handling, SLA tracking |
| ML Frameworks | scikit-learn, XGBoost, PyTorch, TensorFlow, Hugging Face | Model quality, training speed, ecosystem maturity, deployability |
| MLOps | MLflow, SageMaker, Vertex AI, Kubeflow, Weights & Biases | Reproducibility, registry, serving, monitoring, cost controls |
| Observability | Great Expectations, Monte Carlo, Datadog, Grafana | Coverage of failure modes, alerting quality, time-to-detect |
| Visualization | Looker, Tableau, Power BI, Metabase, Streamlit | Semantic modeling, governance, self-serve adoption, performance |
Our delivery model is built to reduce risk and create transferable capability. You own everything we build — code, infrastructure, documentation, and the skills to run it. No black boxes.
Discovery & Architecture
We map your data landscape, interview stakeholders, and document current-state pain points. Output is a target architecture, reference pipeline, and a phased roadmap with clear success metrics.
Foundation Sprint
We stand up the core platform: landing zones, CI/CD, catalog, observability, and the first production pipeline end-to-end. This proves the architecture works before scaling.
Build & Scale
Parallel workstreams migrate and build out pipelines, models, and dashboards. Weekly demos keep stakeholders aligned. Automated tests and code reviews gate every merge.
Productionize & Handover
Runbooks, documentation, on-call procedures, and training sessions transfer ownership to your team. We stay on for optional managed operations or advisory retainer.
Continuous Optimization
Quarterly platform health checks identify cost savings, performance tuning, and capability gaps. The platform evolves with your business, not against it.
The same foundational platform supports dozens of use cases. Below are the patterns we ship most often — each one a proven starting point that we tailor to your domain.
| Use Case | Engineering Component | Science Component | Business Outcome |
|---|---|---|---|
| Customer 360 & Personalization | Unified customer profile, event stream, feature store | Recommendation and propensity models | Higher conversion, retention, and LTV |
| Demand Forecasting | Time-series warehouse, external data enrichment | Hierarchical forecasting, scenario modeling | Reduced stockouts, lower inventory cost |
| Fraud & Risk Detection | Real-time event processing, low-latency serving | Anomaly detection, graph-based risk scoring | Lower fraud losses, faster investigation |
| Operational Analytics | Reverse ETL, semantic layer, embedded dashboards | Benchmarking, anomaly alerting | Faster operational decisions at scale |
| Document Intelligence | OCR pipeline, document store, human review queue | NER, classification, extraction LLMs | Reduced manual processing, faster cycle times |
| Predictive Maintenance | IoT ingestion, time-series storage, edge processing | Remaining-useful-life models, anomaly detection | Reduced downtime, optimized maintenance spend |
| Marketing Mix Optimization | Spend aggregation, attribution warehouse | Causal inference, budget optimization | Higher ROI on marketing spend |
| Generative AI Assistants | RAG pipeline, vector store, guardrail layer | LLM orchestration, eval harness, fine-tuning | Faster knowledge access, lower support volume |
The fastest way to understand whether we can help is a 30-minute architecture review. We will look at your current data landscape, identify the highest-leverage improvements, and give you a concrete recommendation — whether or not you engage us.
What to prepare for the first conversation: a short description of your current data stack, the top two or three decisions you wish were better informed by data, and any known pain points — latency, reliability, trust, or cost. That is enough for us to bring specific recommendations to the table from minute one.
Serving Clients Across the US & UK
Dev Station Technology partners with startups, enterprises, and development teams throughout the United States and the United Kingdom. Our Vietnam-based engineering teams offer significant time-zone overlap with both US Eastern/Pacific and UK GMT business hours, ensuring real-time collaboration and faster delivery cycles. We bill in USD and GBP, comply with US regulations (SOC 2, HIPAA) and UK/EU standards (GDPR, ISO 27001), and provide dedicated account management for North American and British clients.
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