01
Modern Data Stack Architecture
Blueprint your data stack end-to-end: source systems, ingestion (Fivetran, Airbyte, custom Python), warehouse (Snowflake, BigQuery), transformation (dbt), orchestration (Dagster) and downstream activation. We document it clearly enough that engineering and analytics build against the same picture rather than negotiating it project by project. The foundation scales with new data sources instead of fragmenting under them, and the team knows where to extend without rebuilding.
02
Data Pipelines & ELT
Automated ELT pipelines from source systems to warehouse using Fivetran, Airbyte or custom Python where the off-the-shelf connectors don't fit. They re-run safely, handle schema drift and recover gracefully when an upstream system changes its auth flow or rate limits. Engineering hours redirect from chasing data outages to building features, and the team learns about breakage from monitoring instead of from a leadership question.
03
Data Warehouse & dbt
Snowflake or BigQuery with dbt-managed transformations across staging, intermediate and marts layers. Models ship tested, version-controlled and contract-driven, so a changed metric definition propagates everywhere it appears rather than diverging silently across reports. One number for revenue, one for active customers, one for retention, used consistently across finance, marketing and ops.
04
Reverse ETL & Data Activation
Push warehouse data back into the operational tools where it gets used: CRMs, ad platforms, support systems, marketing automation. We model audiences, customer attributes and product signals in dbt, then sync them through Hightouch, Census or native connectors so go-to-market teams act on the same numbers analysts report. The result is one definition of customer that travels everywhere, instead of every team building their own copy.
05
Streaming & Real-Time Pipelines
Streaming infrastructure for the use cases that genuinely need it: real-time personalization, fraud signals, operational dashboards and event-driven workflows. We use Kafka, Kinesis or warehouse-native streaming where it fits, and don't oversell real-time when batch will do. The result is data showing up exactly as fast as the decision requires, without the operational overhead of a streaming stack you don't need.
06
Workflow Orchestration
Orchestration for the data workloads that need scheduling, dependency management and lineage across the stack: Dagster, Airflow or warehouse-native scheduling depending on what the rest of the platform supports. We build the asset graphs, retry policies and observability views that make pipeline failures debuggable rather than mysterious. The result is a data platform where jobs run on time, recover predictably and tell you what they did when leadership asks.
07
ETL Modernization & Migration
Migrate from legacy ETL tooling (Informatica, SSIS, hand-rolled cron jobs) to modern ELT patterns built on Fivetran, Airbyte, dbt and warehouse-native compute. We map every existing job, identify the dependencies that matter and cut over incrementally so the business never loses access to a critical report. The result is a stack that's faster to extend, cheaper to run and a data team that isn't constantly babysitting brittle infrastructure.
