Data Engineering & ETL

AIGard builds the pipelines that collect, clean and move your data, in batch or real time, so analytics and AI can rely on it. We design for quality, cost and observability.

The work typically covers:

• Batch and streaming pipelines
• ETL and ELT development
• Data quality checks and validation
• Orchestration and scheduling
• Pipeline monitoring and alerting
• Documentation and lineage

Data Engineering & ETL is usually the right investment in one or more of these situations:

• Reports disagree because data is inconsistent
• Data arrives too late to act on
• Pipelines fail silently
• You're preparing data for AI

On a 30-minute scoping call we confirm whether it fits your case and recommend a team shape and first milestones.

Yes, AIGard can fix existing data pipelines. We audit them first, then stabilize them, add monitoring and rebuild only what needs it.

The audit looks at why pipelines fail silently or produce inconsistent data, and sound components are kept. We add data quality checks, monitoring and alerting so failures are visible, and we document the pipelines and their lineage for your team.

Real-time data pipelines cost more to run than batch, so AIGard recommends real time only where the business needs data within seconds or minutes.

We build both batch and streaming pipelines and can combine them, applying real time to the specific data flows that justify the cost. The decision is made during scoping, based on how quickly each team needs to act on its data, and is set out in the written proposal.

With the decisions the business needs to make, then the data required to support them.

We assess your sources, quality and existing platforms first, and tell you plainly what your data can and cannot support before recommending an architecture.

PII masking, encryption and audit logging are built into data work, and engineers operate inside your environment under least-privilege access.

Data-handling requirements are agreed during scoping and checked before release.

For Data Engineering & ETL, AIGard engineers typically work with Airflow, dbt, Kafka, Spark, Snowflake, BigQuery, Databricks, AWS Glue and Python.

The stack is selected to fit the systems you already run, and engineers are matched to it before they join.

Data Engineering & ETL is available through any of AIGard’s three engagement models: staff augmentation, where engineers join your team; a dedicated team that runs the workstream; or software outsourcing, where AIGard delivers a defined project.

The choice depends on how much of the delivery you want to manage, and it can change later under the same agreement.

Engineers typically start within two to four weeks of the scoping call. The exact date depends on the roles, seniority and number of people required.

We confirm a realistic start date in writing before you commit, and you meet the proposed engineers before work begins.

AIGard engineers typically cost 30–50% less than the fully loaded cost of a comparable US-based hire, with no recruiting fees, benefits overhead or insurance costs on your side.

The figure for a specific engagement depends on the model, the roles, the seniority mix and the size of the team. We issue a written proposal after the scoping call, so the commercial terms are agreed before any work starts.

NDAs are signed before any access is given, and engineers work inside your environment under least-privilege access, with permissions limited to their responsibilities.

Access is reviewed as roles change and removed when work ends. Our practices align with ISO 27001, SOC 2, GDPR and HIPAA, with PII masking, encryption and audit logging built into data work. We review your access policies, data-handling needs and vendor requirements during scoping.

You do. Intellectual property assignment is written into every contract, in every engagement model.

That covers source code, designs, documentation, data, models and prompts produced for you. Third-party components and their licenses are identified, so ownership is clear at handover.