Data Engineering

What we build

• Data Infrastructure — Multi-tier S3 architectures, AWS Glue catalogs, and storage optimization
• Pipeline Platforms — Event-driven frameworks with Lambda, Step Functions, and orchestration
• Real-Time Processing — Streaming infrastructure for telemetry, analytics, and operational monitoring
• Data Governance — Lake Formation permissions, CloudWatch logging, and audit trails
• Collaboration — Pairing with your data engineers to build production-grade platforms

Infrastructure for data teams

Data platforms require solid infrastructure engineering—not just ETL scripts. We build the foundations that your data engineers work on: storage architectures, pipeline frameworks, observability, and operational tooling.

Best results come from collaboration. We pair with your data engineers to build platforms that handle the infrastructure complexity (error handling, monitoring, cost optimization) while they focus on the data transformations and business logic.

Who this is for

Companies with data engineers who need solid infrastructure to build on. You have the domain expertise for transformations and analytics—you need the platform infrastructure that makes it production-ready.

If your data team is spending more time fighting AWS than analyzing data, or your S3 buckets are an unorganized mess with no clear architecture, you need platform engineering for your data infrastructure.

Data architecture patterns

Multi-Tier Data Lake

• Raw zone: Immutable source data (JSON, CSV, logs)
• Processed zone: Cleaned, validated, deduplicated data (Parquet)
• Curated zone: Business-logic transformed, query-optimized
• Archive zone: Compressed historical data with lifecycle policies

Event-Driven Pipeline Infrastructure

• S3 event notifications triggering Lambda processing
• Step Functions orchestrating complex workflows
• DLQ (Dead Letter Queues) for error handling
• CloudWatch metrics and alerts for pipeline health

Real-Time Streaming

• Kinesis Data Streams for high-throughput ingestion
• Lambda processing with batching and windowing
• Time-series databases (InfluxDB, Timestream)
• Real-time dashboards and alerting

Query Optimization

• Parquet columnar format with Snappy compression
• Hive-style partitioning (year/month/day)
• AWS Glue Data Catalog for schema registry
• Athena query optimization and cost control

Data governance and security

Production data platforms need proper access controls, audit trails, and compliance capabilities. We implement IAM policies for access management, CloudTrail for audit logging, and encryption at rest and in transit.

• IAM policies and bucket policies for access control
• KMS encryption for all data at rest
• VPC endpoints for private S3 access
• CloudWatch Logs for pipeline observability

What you inherit

A production-ready data platform with complete documentation, operational runbooks, and cost monitoring dashboards. Your team gets infrastructure-as-code for all components, making changes auditable and repeatable.

No more wondering "who changed what" or "how does this pipeline work." Everything is version-controlled, documented, and observable.

See our work for examples of data infrastructure projects and pipeline implementations.