Data Pipelines & System Integrations

Project Overview

Businesses were operating disconnected systems — CRMs, ERPs, data warehouses, marketing tools — with no reliable data flow between them. The goal was to automate data movement, ensure consistency across systems, and support both real-time and batch processing so teams could make decisions from a single, up-to-date picture.

Timeline: 5 months (discovery, design, build, hardening). Role: Lead data engineer — pipeline architecture, connector development, transformation logic, and orchestration. Tech stack: Python, Apache Airflow / Prefect for orchestration, PostgreSQL for state and metadata, REST and GraphQL APIs, AWS S3 / GCP for staging, message queues (Kafka / RabbitMQ), and dbt for transformation layers.

The Challenge

Data was duplicate and inconsistent across platforms. Manual CSV exports were the primary "integration" method, with no error handling or retry logic when transfers failed. There was no way to trace where data broke or was transformed incorrectly, and scaling became a bottleneck as volume grew. We needed pipelines that were idempotent, observable, and resilient from day one.

Flow 1 — End-to-End ETL Pipeline

Swimlane view: data sources through ingestion, transform, and load.

End-to-End ETL Pipeline Flow

Data Sources

CRM API DB Replica S3 Bucket Webhooks

Ingestion

Extractor File Parser Stream Reader Queue Ingest

Transform

Cleanse Normalize Enrich Validate Schema Registry

Load

Data Warehouse Analytics DB REST API Push BI Dashboard

Flow 2 — Error Handling & Retry Logic

Decision flow with branching and dead-letter handling.

Error Handling & Retry Logic Flow

Pipeline Triggered Run Task

Yes → Next Task No → Log Error

Retry Dead Letter Queue

Alert: Slack / Email

Manual Review Queue

Flow 3 — Multi-System Integration Architecture

Hub-and-spoke: middleware connects sources to destinations.

Multi-System Integration Architecture

CRM: Salesforce ERP: NetSuite

API Connector DB Connector

Integration Middleware
Transform / Route / Log

Data Warehouse
BigQuery Marketing
HubSpot Support
Zendesk

Solution Walkthrough

Stage 1

Pipeline architecture

Chose batch vs. streaming per use case: batch for reporting and bulk sync (Airflow DAGs), streaming for event-driven updates (Kafka consumers). Defined idempotency keys and checkpointing so reruns were safe.

Stage 2

Source connectors & ingestion

Built connectors for REST/GraphQL APIs, DB replicas, S3/GCS files, and webhooks. Each connector normalizes to an internal schema and publishes to a queue or writes to staging for the transform layer.

Stage 3

Transformation & loading

Transform logic cleanses, normalizes, and enriches; dbt models handle warehouse-specific SQL. Load step writes to the data warehouse, pushes to downstream APIs, or updates BI datasets with incremental strategies.

Stage 4

Scheduling, monitoring, alerting

Airflow/Prefect schedules DAGs; we added health checks, SLA alerts, and dashboards for run history. Failures trigger retries (with backoff) and then route to a dead-letter queue and Slack/email.

Stage 5

Data lineage & audit logging

Every run logs source, destination, row counts, and checksums. Lineage is stored in metadata so we can trace any record back to its origin and detect schema drift or breaking API changes.

Technical Deep-Dive

Sample patterns: ETL structure, webhook trigger, validation.

def run_pipeline(source: str, destination: str):
    raw_data = extract(source)           # Pull from API or DB
    clean_data = transform(raw_data)     # Normalize & validate
    load(clean_data, destination)        # Push to target system
    log_run(source, destination, len(clean_data))

@app.route("/webhook/trigger", methods=["POST"])
def handle_webhook():
    payload = request.json
    if validate_signature(payload):
        pipeline.trigger(payload["event"], payload["data"])
        return {"status": "queued"}, 202
    return {"error": "unauthorized"}, 401

def validate_record(record: dict) -> bool:
    required_fields = ["id", "email", "created_at"]
    return all(
        field in record and record[field] is not None
        for field in required_fields
    )

Infrastructure Overview

Three lanes: Ingest → Process → Deliver.

Ingest

CRM DB APIs Files

Process

Pipeline Orchestrator Queue Transformer Validator

Deliver

Data Warehouse Dashboard Downstream APIs

Results & Impact

Before

Manual CSV exports and ad-hoc scripts between systems
No error handling or retry; failures required manual intervention
No visibility into where data broke or how it was transformed
Scaling blocked by single-threaded runs and no checkpointing

After

15+ automated pipelines moving data between CRM, ERP, warehouse, and BI
99.8% pipeline reliability with retries and dead-letter handling
10M+ records processed monthly with full lineage and audit logs
Engineering hours saved per week on manual data handling

Metric	Before	After
Records processed (monthly)	~500K (manual)	10M+ (automated)
Manual data handling (hrs/week)	~12	~1
Error rate (failed runs)	High, untracked	<0.2%

"We finally have data we can trust. The pipelines run on their own, and when something fails we know exactly where and why."

— Data lead (client)

Lessons Learned

• Idempotency: Every pipeline step was designed to be idempotent (e.g. upsert by key, append with dedupe). Reruns and retries became safe and predictable.
• Designing for failure: We assumed tasks would fail and added retries, backoff, dead-letter queues, and alerting from the start. That mindset reduced firefighting later.
• Monitoring as first-class: Run history, row counts, and latency dashboards made it easy to spot drift and regressions before stakeholders noticed.
• Schema evolution: APIs and DBs change. We added schema registry checks and versioned contracts so breaking changes were caught in staging and could be handled with migration scripts.