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Data Engineering

Data Systems Teams Actually Trust

If teams don't trust the data, they won't use it. Pipelines fail silently, dashboards show stale numbers, and everyone reverts to spreadsheets. We build data systems with validation, monitoring, and clear lineage—because we depend on our own daily.

Why This Breaks in Practice

  • —Pipelines fail silently with no alerts. Teams discover issues days later when reports don't match reality.
  • —Data quality degrades over time. Duplicates accumulate, formats drift, and validation happens too late to prevent corruption.
  • —Schema changes break downstream systems. One source system update cascades failures across reporting and analytics.
  • —Dashboards show conflicting numbers. Different queries produce different results because lineage is unclear and transformations are undocumented.
  • —Reports aren't used. Dashboards built for demos rather than daily operations get ignored because they don't answer real questions.
  • —Integration sync issues compound. Records duplicate across systems, updates fail to propagate, and nobody knows the source of truth.

Our Approach

Quality gates at ingestion. Validation, deduplication, and schema checks happen before data enters the warehouse, not after problems surface.

Observable pipelines. Every transformation has logging, monitoring, and alerts for failures, latency spikes, and data anomalies.

Clear data lineage. Transformations are documented and version-controlled so teams understand where numbers come from.

Built for daily use. Dashboards answer operational questions teams actually ask, not generic metrics that look good in screenshots.

Incremental processing. Pipelines handle backfills, late-arriving data, and schema evolution without full rebuilds.

Reasonable tech choices. Standard tools and patterns that can be maintained by your team, not exotic stacks that require specialists.

What We Build

Data Pipelines

ETL and ELT workflows that move and transform data reliably.

  • • Ingestion from APIs, databases, and third-party sources with rate limiting and retry logic
  • • Transformation pipelines with validation, deduplication, and schema enforcement
  • • Incremental processing with checkpointing for late-arriving or updated data

Operational Dashboards

Real-time and near-real-time reporting for daily operations.

  • • Metrics dashboards showing pipeline health, data freshness, and quality scores
  • • Business dashboards for sales, operations, and customer metrics used daily
  • • Alerting on data anomalies, missing data, or metric thresholds

Data Warehouses

Centralized storage with consistent schema and clear access patterns.

  • • Dimensional modeling for consistent reporting and analysis
  • • Access control and row-level security for sensitive data
  • • Documentation of data lineage and transformation logic

Analytics Instrumentation

Event tracking and product analytics for user behavior and system performance.

  • • Event schema design and validation for product analytics
  • • Integration with analytics platforms (Segment, Mixpanel, PostHog)
  • • Custom reporting on user flows, feature adoption, and retention

Built and Operated Internally

We're a small team, but we run data systems daily. PingSLA ingests monitoring data, processes webhooks, and surfaces uptime metrics in real-time. Rizqtek CRM tracks customer workflows, pipeline health, and operational metrics with full audit trails. These systems inform our actual operations, not demos.

When we build data systems for others, we apply the same discipline. We know what makes dashboards useful because we look at them every morning. We know what validation catches real issues because we've debugged pipelines that failed silently. We know what monitoring matters because we've been alerted at the right time—and the wrong time.

This operational experience means we build systems that stay maintained. Clear documentation, standard patterns, and reasonable complexity. No over-engineered solutions that become unmaintainable six months later.

Let's Talk

If your data systems need to be more reliable or your team needs dashboards they'll actually use, we should have a conversation. We'll discuss your data sources, identify what matters operationally, and see if we're a good fit.

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