AWS Data Engineering
The data layer everything else sits on. Pipelines, warehouses, governance, streaming, and the retrieval layer AI grounds on — production-grade, observable, and ready for analytics and LLM workloads to plug into without rebuilding.
Principles that drive the engineering
Six rules we hold for every data platform we build. They're why pipelines we shipped years ago are still feeding dashboards and AI features today.
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Data is a product, not a byproduct
Every dataset has an owner, a consumer contract, and an SLA. We design for who uses the data, not for whoever happens to query it today.
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Governance before access, not after breach
Lake Formation, row-level security, and audit trails are part of the first landing — not a compliance project we run after an incident.
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Ingest idempotently, transform incrementally
Replays are safe. Backfills are routine. Retries don't create duplicates. The pipeline you built yesterday still runs tomorrow.
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Schema evolution is a first-class problem
Iceberg tables, migration scripts, and version contracts. Columns change, sources break, data shifts — we design for it instead of reacting to it.
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Observability over dashboards
Freshness checks, row-count drift, DLQs, and lineage — surfaced before the business asks why the report looks wrong. Dashboards alone don't catch silent failures.
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Pipelines outlive the engineer who wrote them
Code reviews, documentation, runbooks, and tests. We build pipelines the next engineer can maintain — yours or ours.
AWS Data Engineering, end to end
Four modes shape every data platform we deliver: ingest, transform, serve, govern. Together they're the data and retrieval layer analytics, reporting, and AI ground on.
Source integration
SaaS APIs, operational databases, SFTP drops, and third-party feeds — landed in one trustworthy platform that product and analytics can share.
Real-time streaming
Kinesis, Lambda, and event-driven patterns for use cases where yesterday's data isn't good enough — with backpressure, DLQs, and replay built in.
ETL / ELT workflows
AWS Glue, Step Functions, and dbt for clean, analytics-ready transformations. Lineage and testing included, not sold separately.
Warehouse, lakehouse & retrieval
Redshift, Athena, Iceberg on S3 — sized for cost and performance with partition layouts and query patterns documented from day one. Vector stores and Bedrock Knowledge Bases where retrieval has to ground an LLM in current, governed data.
Pipeline design and build
Batch and streaming pipelines with full observability — Glue, Step Functions, Lambda, Kinesis — and the retries, DLQs, and alerting you'll actually want at 2 a.m.
Data quality and governance
Validation, lineage, access controls with Lake Formation and IAM, and audit trails your security team will sign off on.
What we reach for, and why
Kinesis for streams, Glue for scheduled loads, Lambda and Step Functions for event-driven ingest. SaaS APIs wrapped in idempotent connectors.
dbt as our default transformation framework. Glue and Spark for heavy lift, Step Functions for orchestration. Lineage and tests in-code.
Iceberg on S3 is our default for lakehouse; Redshift for structured analytics at scale; Athena for ad-hoc and serverless query patterns.
Lake Formation for fine-grained access. IAM for identity. Glue Data Catalog as the single source of schema truth.
DynamoDB for high-throughput key-value, RDS and Aurora for transactional systems that feed the analytics layer.
CloudWatch dashboards and alarms for pipelines, freshness checks and row-count drift in dbt tests, DLQs wired into alerting.
The way a project actually runs
From data audit to production platform in four phases, each with a tangible deliverable you review before we move to the next.
Audit the data estate
Sources, quality, governance, and the infrastructure they live on today. Name what's blocking analytics and AI before picking tools.
Architect the platform
Warehouse or lakehouse, ingestion patterns, governance model, service levels. Decisions documented and signed off.
Build with observability
Terraform from the first commit. Tests, lineage, DLQs, and alerts wired in at build time. Production-ready from the first pipeline.
Hand over and extend
Runbooks, documentation, and training. Optional continuation into managed operations or extensions as new sources and consumers arrive.
Seen in production
Reporting Dropped from Three-to-Six Months to Hours
Integrated portal and governed data platform replaced a decade of spreadsheet reporting — multi-month reports now take hours.
Read the case studyFrom On-Prem Monolith to Modern AWS in 10 Weeks
Fast-growing U.S. healthcare services provider migrated from on-prem to AWS in ten weeks across three SOWs, assessment-first — zero unplanned downtime.
Read the case studyPart of these solutions
Data engineering feeds AI Enablement and Application Development — and stands alone for reporting platforms, data migrations, and compliance-driven data modernization.
Need the data and retrieval layer under your AI?
Tell us where your data lives today and what you want to do with it. We'll map the gaps, scope the pipelines, and come back with a platform plan — production-grade, observable, and ready for analytics and AI.
Book a Discovery Call