Data Engineering for ML
We build ingestion, transformation, and feature pipelines with contracts, quality checks, and observability—so data science and ML teams spend time on models, not firefighting bad joins or stale tables.
Data foundations that keep ML and AI features honest.
Phases
4-phase program
Timeline
commonly 6–12 weeks depending on sources and environments
Outcomes
3 target deliverables
Problem framing
Where teams lose leverage
Models fail quietly when training data diverges from production, schemas drift, and nobody owns the path from source systems to model features. Strong ML starts with engineered data.
- 1
Siloed spreadsheets and ad-hoc SQL block reproducible training and audits.
- 2
No lineage or freshness SLAs means silent degradation in production models.
- 3
Feature definitions live in notebooks instead of versioned, shared pipelines.
Target outcomes
What this engagement delivers
Documented data contracts between product, analytics, and ML consumers
Batch and streaming pipelines with monitoring on volume, latency, and quality
Feature stores or curated tables your models can trust at train and serve time
Scope
Deliverables we commit in writing
Exact backlog is tailored in discovery; below is representative of what enterprise buyers typically require for acceptance.
Source-to-curated ETL/ELT with dbt-style testing and ownership boundaries
Lakehouse and warehouse integration (Snowflake, BigQuery, Redshift, object storage)
Feature pipeline design aligned to how models will be trained and served
Data quality gates: schema checks, null thresholds, anomaly alerts
Lineage and documentation your team can extend after handoff
Program structure
Phased delivery model
Milestones map to artifacts you can review with engineering, security, and finance stakeholders.
Week 1–2
Landscape & contracts
Sources, consumers, SLAs, and sensitivity classification.
Weeks 2–7
Core pipelines
Ingestion, transforms, curated layers, and quality tests.
Weeks 6–9
Feature readiness
Tables or features aligned to upcoming model work.
Week 9+
Operate & handoff
Runbooks, on-call hooks, and KT to your data owners.
Reference view
Logical architecture
Your production topology will reflect your cloud, identity, and data residency choices — this diagram communicates control points and trust boundaries we design around.
Technology
Typical stack (vendor-neutral)
We standardize on primitives your team can operate — and avoid stack-lock where it hurts maintainability after handoff.
Indicative timeline
Foundational pipelines: commonly 6–12 weeks depending on sources and environments
Final scope depends on your data maturity, integration count, and compliance requirements — all defined in the written SOW.
Get a scoped estimateGovernance
Security and compliance posture
We implement technical controls and documentation suitable for enterprise procurement — not checkbox theater.
Role-based access to raw and curated layers with audit-friendly logs
PII handling and retention policies applied before downstream ML use
Reproducible pipeline runs with pinned dependencies and environment separation
Procurement
Statements of work, change control, and optional penetration-test windows are scoped explicitly. Legal sign-off remains with your counsel.
FAQ
Technical and commercial questions
Data Engineering for ML
Ready to scope this engagement?
Thirty-minute discovery call. Fixed written scope within a week. No open-ended hourly burn.