We help organizations design, build, and operate modern data platforms so that data is reliable, secure, and ready for analytics and AI. Our data engineering consultants turn fragmented, legacy data into a governed, scalable foundation for reporting, machine learning, and real‑time decision‑making.
Core services
- Data platform architecture
- Design modern data lake/lakehouse and data warehouse architectures, on‑prem and cloud.
- Select and integrate core technologies (e.g. Databricks, Synapse/Fabric, Snowflake, Kafka, dbt, Airflow).
- Define patterns for batch, micro‑batch, and streaming workloads
- Build and automate ETL/ELT pipelines from databases, APIs, files, and streaming sources.
- Implement workflow orchestration, scheduling, and CI/CD for data pipelines.
- Optimize for performance, scalability, and observability
- Data pipeline development
- Data integration and modernization
- Consolidate data from enterprise systems (ERP, CRM, line‑of‑business apps) into a central platform.
- Migrate legacy warehouses and ETL jobs to cloud‑native or lakehouse architectures.
- Implement CDC and event‑driven integration where low latency is required.
- Data quality, catalog, and governance
- Set up data profiling, validation rules, and automated quality checks in pipelines.
- Implement lineage tracking, logging, and monitoring across data flows.
- Deploy cataloging and metadata management with access control and compliance built in.
- DataOps and platform operations
- Apply DevOps practices to data: IaC, automated testing, and version control for pipelines and schemas.
- Establish monitoring, alerting, and incident management for data workloads.
- Provide runbooks, SLAs, and operational best practices.
- AI‑ready data foundations
- Prepare curated, well‑modeled datasets and features for ML and BI.
- Ensure data quality, latency, and governance meet AI and regulatory requirements.
- Connect data pipelines to downstream analytics, ML platforms, and BI tools.
Typical engagement model
- Phase 1 – Discovery & assessment (2–4 weeks)
Assess current data landscape, tools, pain points, and business priorities. Deliver a current‑state map, gap analysis, and prioritized roadmap. - Phase 2 – Target architecture & blueprint (2–3 weeks)
Define target data platform architecture, technology choices, and reference patterns for ingestion, storage, transformation, and access. - Phase 3 – Implementation & migration (6–12+ weeks)
Build foundational pipelines, stand up the platform, and migrate critical workloads, with automated tests and monitoring. - Phase 4 – Handover & enablement (ongoing or 2–4 weeks)
Knowledge transfer, documentation, training for internal teams, and optional managed services or CoE support.