Data Orchestration
Pipelines that run when they're supposed to.
Workflow orchestration for complex data pipelines — scheduling, dependencies, retries, and observability built in from day one.
A data pipeline that runs once in development is not a data pipeline — it is a script. A production orchestration layer is what turns scripts into systems: managed scheduling, dependency-aware execution, automatic retry on failure, alerting when SLAs are missed, and the visibility to know what ran, when it ran, and why it failed. We build those systems.
What we build
Orchestration tooling has matured significantly. The right choice depends on your existing infrastructure, your team's operational preferences, and what the pipelines actually need to do. We work across the major orchestration platforms and select based on fit, not familiarity.
Airflow DAGs
Apache Airflow remains the most widely deployed orchestration platform in enterprise data environments, and for good reason — its DAG model is expressive, its operator ecosystem is broad, and the operational knowledge base around it is deep. We write production Airflow DAGs with appropriate task decomposition, dependency graphs, retry policies, and connection management. We also inherit and refactor existing Airflow environments that have grown into complexity without intentional design.
Dagster Assets and Jobs
Dagster's asset-centric model is the right choice for teams building data pipelines where the primary concern is the freshness and correctness of data assets, not just task execution. We build Dagster asset graphs that model your data dependencies explicitly, configure auto-materialization policies, and integrate with Dagster's built-in data lineage and quality observability. For new data platform builds, Dagster is often the right choice.
Prefect Flows
Prefect's Python-native flow definition and its managed execution environment make it a good fit for teams that want orchestration without the operational overhead of running and maintaining their own scheduler. We build Prefect flows with appropriate task structure, caching policies, concurrency controls, and integration with Prefect Cloud for visibility and alerting.
Temporal Workflows
Temporal is the right orchestration layer when your workflows need durable execution — long-running processes that must survive server restarts, network failures, and arbitrary delays. We build Temporal workflows for data pipelines that span hours or days, have complex compensation logic for failure scenarios, or integrate with external systems that require reliable exactly-once or at-least-once execution semantics.
Pipeline Monitoring and Alerting
Knowing a pipeline failed is the floor, not the ceiling, of orchestration observability. We build monitoring systems that give you meaningful context: which task failed, what the error was, what the upstream and downstream dependencies are, and what the business impact of the failure is. Alerts go to the right channel with enough context that the on-call person can understand and act, not just know something is wrong.
SLA Tracking
Data pipeline SLAs — "the warehouse must be refreshed by 7am" or "this report must be available before the 9am business review" — are commitments to the business that require explicit tracking, not assumptions. We implement SLA tracking that monitors expected completion windows, fires early warnings when pipelines are at risk of missing their window, and escalates appropriately when they do.
Backfill and Recovery
Pipelines fail, sources change, and data needs to be reprocessed. We design backfill and recovery mechanisms into orchestration systems from the start — idempotent tasks that can be re-run safely, partitioned execution that allows targeted reprocessing of specific time windows, and recovery workflows that restore pipeline state correctly after failure without producing duplicate or inconsistent data.
Cross-System Dependency Management
Enterprise data environments are rarely served by a single orchestration system. We design cross-system dependency management so that pipelines in different orchestration platforms can express and respect dependencies on each other — a dbt model that should not run until an Airbyte sync completes, a downstream ML pipeline that should not start until the feature store is refreshed.
Orchestration Stack
We work across the major orchestration platforms and select based on fit — your infrastructure, your team's operational preferences, and what your pipelines actually need to do.
Airflow
Best for complex DAG-based pipelines with rich ecosystem integrations.
Dagster
Best for data-asset-centric pipelines with built-in observability.
Prefect
Best for Python-native teams wanting dynamic, code-first orchestration.
Temporal
Best for long-running, durable workflows requiring fault-tolerant state.
Custom & Event-Driven Orchestration
Event-driven pipelines using Kafka triggers, webhook-initiated workflows, and custom schedulers for workloads that do not fit the DAG or asset-centric model. When the standard orchestration tools introduce more complexity than they remove, we design the right lightweight alternative.
How We Design Pipelines
Pipeline failures are usually not random. They are the predictable result of unclear dependencies, undefined failure modes, and insufficient observability. We address all three before writing the first task.
1. Dependency Mapping
We map every upstream and downstream dependency before building. Unclear dependencies are the primary source of silent pipeline failures — a task that appears to succeed but produces incorrect results because a dependency it assumed was satisfied was not. The dependency map drives the DAG structure or asset graph before any code is written.
2. Failure Mode Design
We define what happens when each step fails before writing the happy path. Retry logic, dead letter queues, alerting thresholds, and backfill strategies are designed up front — not added after the first production incident. Pipelines built this way recover cleanly. Pipelines that skip this step require manual intervention every time something unexpected happens.
3. Incremental Build with Testing
Each DAG, asset, or flow is tested in isolation before being wired to its dependencies. We do not integrate first and test later. Integration tests run against real data in a staging environment that mirrors production topology, not a simplified approximation.
4. Observability First
Every pipeline ships with dashboards covering lag, failure rate, and SLA tracking. Alerts fire with enough context that the on-call person can understand and act — not just know something is wrong. If something breaks at 3am, you know before your users do.
What we orchestrate
Orchestration is needed wherever the business depends on data processes running reliably on a schedule. These are the patterns we see most often.
Coordinated orchestration of ELT syncs, dbt transformations, and data quality checks in the correct dependency order so the warehouse is fresh, correct, and validated when the business needs it.
Feature engineering, model training, evaluation, and deployment pipelines that run on schedule or event trigger, with automatic promotion logic and rollback on degraded model performance.
Scheduled extract-transform-load pipelines from legacy systems, third-party data providers, and operational databases — with retry logic, error handling, and alerting when data does not arrive on time.
Pipelines triggered by external events — a new file landing in object storage, a webhook from a SaaS platform, or a condition met in a monitoring system — rather than purely on a schedule.
Scheduled pipelines for regulatory reporting — assembling, validating, and formatting data for submission — with audit trails, approval gates, and documentation that satisfies compliance review.
Why AR Data
Data orchestration is invisible when it works and catastrophic when it fails. The organizations we have worked with — from Iron Mountain's records management infrastructure to enterprise data systems at Oracle and Scotiabank — run on scheduled data processes that the business depends on daily. We understand what it means to build these systems to a standard where failure is genuinely rare and recovery is fast when it happens.
Enterprise delivery background means we build orchestration systems that are maintainable by the team that inherits them — not just systems that work for the person who built them. That includes documentation, operational runbooks, clear naming conventions, and monitoring that surfaces meaningful information rather than just noise.
We use agentic workflows in our own build process. We deliver faster than a traditional shop without reducing quality. Fixed-scope engagements mean you know what you are getting before we start.
Ready to orchestrate your data pipelines?
30 minutes. We scope your pipeline complexity, your SLAs, and what production orchestration looks like for your environment. No pitch deck.
