Production engineering depth, mapped to the systems you run.
Capabilities supporting healthcare platforms, enterprise data systems, distributed processing, AI-agent delivery, and multi-cloud modernization — each one an area we architect, build, harden, deploy, and operate.
Filter by domain. Expand any capability for engineering depth, deliverables, reliability posture, and business impact.
Healthcare Data Platforms & InteroperabilityHealthcare+
Clinical, claims, and operational data platforms that integrate fragmented systems into a governed, HIPAA-aware foundation clinical and executive stakeholders can trust.
Problems solved
- Fragmented, untrusted clinical/operational data
- Manual, un-auditable reporting
- No lineage or single source of truth
Systems & deliverables
- FHIR/HL7 integration & mapping
- Validation & reconciliation pipelines
- Governed lakehouse & analytics marts
Engineering depth
- Source-to-target completeness checks
- Normalization & data contracts
- Immutable lineage
Reliability & governance
- HIPAA-aware, least-privilege access
- Audit-ready workflows
Business impact: a reliable, auditable foundation for reporting, operations, and regulatory oversight — with faster access to trusted information and responsible AI adoption.
Kafka & Event StreamingDataDistributed+
Real-time event architectures that move operational data reliably between systems while preserving replay, ordering, ownership, and observability.
Problems solved
- Delayed data availability
- Brittle point-to-point integration
- Inconsistent event contracts
- Inability to replay or recover
Systems & deliverables
- Topic & partition design
- Producer/consumer services, CDC
- Schema & data contracts
- Replay & dead-letter workflows, runbooks
Engineering depth
- Partition-key strategy, ordering guarantees
- Consumer-group design, idempotency
- Backpressure, retention, rebalancing
- Failure recovery
Reliability & governance
- Schema registry & ownership
- Monitoring & operational runbooks
Business impact: more reliable real-time data availability, faster integration, reduced operational coupling, and a scalable foundation for analytics and AI.
Databricks, Spark & Lakehouse Engineering — MedallionData+
Databricks platform engineering on a governed medallion lakehouse (bronze → silver → gold) — raw immutable ingest, validated & conformed data, and curated BI/ML-ready marts, all with lineage and cost control.
Problems solved
- Unreliable, ungoverned data platforms
- No separation of raw / conformed / curated
- Analytics & ML blocked by trust
Systems & deliverables
- Bronze/silver/gold layer design
- Spark/PySpark distributed transforms
- Delta Lake & Structured Streaming
- ML-ready feature marts
Engineering depth
- Partitioning, shuffle reduction, cluster tuning
- Data contracts & schema evolution
- Idempotent, restartable, checkpointed jobs
Reliability & governance
- Lineage, cataloging, reconciliation
- Cost-aware compute, observability
Business impact: analytics- and AI-ready data available sooner and more reliably, with governed trust from raw ingest to curated gold and predictable cloud cost.
Distributed Data ProcessingDistributed+
High-throughput distributed pipelines engineered for parallelism, fault tolerance, and horizontal scale — the difference between a demo and a system that survives production.
Problems solved
- Jobs that fail and can't restart cleanly
- Skew, shuffle, and memory pressure
- Non-idempotent reprocessing
Systems & deliverables
- Partitioning & parallel processing
- Streaming state & checkpointing
- Workload orchestration
Engineering depth
- Shuffle reduction, memory/compute optimization
- Idempotency, replay, restartability
- Performance diagnostics
Reliability & governance
- Fault tolerance & recovery
- Horizontal scalability
Business impact: reliable processing of high-volume, high-velocity, mission-critical data with faster recovery and lower compute cost.
Python, SQL & Pipeline EngineeringData+
Production ETL/ELT in Python and SQL — batch and streaming — with schema evolution, data contracts, and restartable, replayable pipelines.
Problems solved
- Fragile, un-restartable jobs
- Schema drift breaking downstream
Systems & deliverables
- Batch & streaming workload design
- Data contracts & schema evolution
Engineering depth
- Checkpointing & replay
- Idempotent design
Reliability & governance
- Reconciliation & parity testing
Business impact: dependable data delivery that downstream analytics, ML, and AI can build on.
Data Quality, Lineage & GovernanceDataSecurity+
Source-to-target completeness validation, reconciliation, anomaly detection, and metadata/lineage that make data auditable and trustworthy.
Problems solved
- Incomplete or unverifiable loads
- No lineage or audit trail
Systems & deliverables
- Completeness & parity validation
- Data-quality rules & anomaly detection
- Cataloging & lineage
Engineering depth
- Bidirectional reconciliation
- Automated quality gates in CI/CD
Reliability & governance
- Auditable, governed data
Business impact: trusted data and audit-ready workflows that reduce regulatory and operational risk.
Enterprise AI Agents & OrchestrationAI Agents+
Tool-using AI agents and multi-agent orchestration with structured outputs, human approval, evaluation, and traceability — production systems, not chatbots.
Problems solved
- AI stuck in experimentation
- Unsafe, ungoverned automation
Systems & deliverables
- Tool calling & task delegation
- State, memory, long-running workflows
- Human approval gates
Engineering depth
- Structured outputs & deterministic validation
- Model routing, latency/cost control
Reliability & governance
- Evaluation, guardrails, tracing
- Least-privilege tool access
Business impact: controlled production adoption that adds organizational capacity while preserving accountability.
RAG, Knowledge Systems & Document IntelligenceAI Agents+
Retrieval-augmented generation over governed knowledge with grounded, cited responses and structured extraction from documents.
Problems solved
- Hallucination & ungrounded answers
- Locked-away enterprise knowledge
Systems & deliverables
- Enterprise RAG pipelines
- Document intelligence & extraction
Engineering depth
- Grounding & citation controls
- Deterministic validation of outputs
Reliability & governance
- Disclosure/claims policy enforcement
Business impact: faster, trustworthy knowledge access with controls that keep answers accountable.
Multi-Cloud & Cloud-Native ArchitectureCloud+
Architecture across AWS, Azure, and Google Cloud — serverless, containers, and event-driven design — selecting the right managed services while keeping patterns and operations standardized.
Problems solved
- Inconsistent per-workload builds
- Cloud lock-in vs. portability tension
Systems & deliverables
- Landing zones & account structures
- Serverless/event-driven & Kubernetes
Engineering depth
- Cloud-neutral patterns where appropriate
- Networking & private connectivity
Reliability & governance
- Standardized security & operations
Business impact: consistent, portable architecture with predictable operations across clouds.
Reusable Infrastructure & Delivery AutomationCloud+
Infrastructure as code with reusable modules, reference architectures, CI/CD, and standardized promotion — repeatable platforms rather than one-off builds.
Problems solved
- Bespoke, slow environment delivery
- Drift and inconsistency
Systems & deliverables
- Terraform modules & reference architectures
- CI/CD, automated validation
- Policy & security guardrails
Engineering depth
- Repeatable provisioning
- Promotion across dev/test/prod
Reliability & governance
- Operational runbooks
Business impact: faster onboarding of teams and workloads, fewer one-off builds, and more predictable releases.
Security, Reliability & Production ObservabilitySecurity & Reliability+
Identity, least privilege, encryption, and secrets management alongside logging, metrics, tracing, autoscaling, disaster recovery, and cost-aware operations.
Problems solved
- Over-broad access & weak auditability
- Slow failure diagnosis
Systems & deliverables
- IAM, secrets, encryption
- Observability stack & alerting
- Backup & disaster recovery
Engineering depth
- Least-privilege architecture
- Failure recovery & autoscaling
Reliability & governance
- Audit & evidence preservation
Business impact: stronger security posture, faster recovery, and cost-aware, resilient production operations.
Machine Learning & AI Data FoundationsAI AgentsData+
Reliable, governed data foundations for ML and generative AI — feature pipelines, distributed feature processing, validation, and reproducible model-ready datasets.
Problems solved
- Untrustworthy, non-reproducible training data
Systems & deliverables
- Feature pipelines & marts
- Distributed feature processing
Engineering depth
- Validation & reproducibility
Reliability & governance
- Lineage for model-ready data
Business impact: AI and ML built on data teams can actually trust.
Match these capabilities to your hardest problem.
Tell us the system you need built or hardened — we'll map the exact capabilities and a delivery plan.