Data Engineering & Analytics
Modern data stacks with governed pipelines, real-time dashboards, and AI-assisted insights to drive decisions at speed.
Governed Data at Scale
We design schemas, data contracts, and testing for accuracy and compliance across batch and streaming. Contracts make upstream/downstream expectations explicit to prevent silent breaks.
Domain ownership is enforced via data products with SLAs, versioning, lineage, and access policies—enabling dependable reuse across the org.
Insights to Action
From BI dashboards to operational triggers, we connect insights to workflows that move metrics. We embed call‑to‑action buttons and alerting to shorten the time from insight to action.
We implement semantic layers and headless BI so metrics definitions are consistent across teams and tools.
Data Strategy & Roadmap
We align stakeholders on business questions, KPIs, and compliance requirements; we define a phased roadmap balancing quick wins with foundational work.
We choose build vs. buy pragmatically and map tools to capabilities—ingestion, transformation, governance, catalog, and observability.
Lakehouse & Warehouse Architecture
We implement lakehouse/warehouse patterns using decoupled storage and compute, supporting both BI and ML workloads.
We organize bronze/silver/gold layers, enforce partitioning and clustering, and manage lifecycle policies to control cost.
Ingestion & Change Data Capture
We use ELT/ETL pipelines and CDC from operational databases to keep analytics fresh without overloading sources.
We build resilient connectors with retries, dead‑letter queues, and backpressure handling for durability.
Transformation & Modeling (dbt)
We organize transformations using dbt, tests, and documentation. We standardize naming, folder structure, and macros for maintainability.
Semantic models define business metrics, hierarchies, and slowly changing dimensions to support consistent reporting.
Real‑Time & Streaming Analytics
For use cases that demand low latency (fraud detection, logistics tracking), we implement streaming with Kafka/Kinesis and materialized views.
We balance freshness, correctness, and cost using windowing, upserts, and compaction strategies.
Quality, Testing & Observability
Data tests (schema, freshness, uniqueness) and anomaly detection protect downstream consumers from bad data.
We add lineage, ownership, and alerts to catch regressions quickly and support audits with confidence.
Security & Compliance
We implement row/column‑level security, tokenization, and masking in accordance with privacy laws (GDPR/CCPA).
Access is enforced via roles/attributes; PII handling and retention policies are automated with audit trails.
Cost Optimization & FinOps
We monitor storage/compute cost drivers, adopt efficient file formats (Parquet/ORC), and tune clustering and compression.
Workload governance (schedules, quotas, concurrency) and warehouse sizing policies keep spend predictable.
ML Enablement
We shape features, maintain feature stores, and expose governed datasets for ML teams—bridging analytics to AI productization.
We add model evaluation datasets and monitoring hooks to support continuous improvement and safe rollouts.
Key Benefits
- Trusted single source of truth with clear data contracts
- Self‑serve analytics and governed semantic layers
- Near real‑time insights through streaming and CDC
- Lower cost via storage/compute decoupling and FinOps
- Higher data quality with tests and observability
- AI‑assisted insights and natural‑language querying
Use Cases
- Executive dashboards and KPI scorecards
- Embedded analytics for customers and partners
- Streaming anomaly detection and alerting
- Revenue operations and product analytics
- Customer 360, churn/risk scoring, and CLTV modeling
Results
We implemented CDC + dbt + a governed semantic layer with observability, raising trust and adoption.
- Dashboard Freshness: <5 min
- Data Incidents: -43%
- BI Adoption: +24%
Frequently Asked Questions
Which tools do you use?
dbt, Snowflake/BigQuery, Kafka/Kinesis, Airflow, Fivetran/Stitch, Looker/Power BI/Superset, plus catalog/observability tools.
How fresh can our data be?
Batch pipelines typically run 5–15 minutes; CDC/streaming can drive sub‑minute freshness depending on SLAs and cost tolerance.
How do you handle privacy and compliance?
We implement RBAC/ABAC, masking, tokenization, consent tracking, retention policies, and audit trails to meet regulatory requirements.
Can you migrate from legacy ETL to a modern stack?
Yes—phased migration with co‑existence; we map legacy jobs to dbt models and deprecate safely with lineage and tests.
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