Data Engineering Services
We build the data foundation that powers AI, analytics, and automation. Our team designs modern, compliant, and cost-efficient data ecosystems across AWS, Azure, and GCP — connecting sources, cleaning flows, and enabling insight-ready pipelines. Expect measurable reliability, faster decisions, and scalable performance — all built for your real business operations, not slides.
Our Offerings
Data Strategy & Roadmapping
We map your data landscape, KPIs, and compliance needs into a 6–12 month plan with clear workstreams, costs, and dependencies. Expect architecture options (lakehouse vs. warehouse), build-vs-buy decisions, and a backlog sized by value and effort. This is where our data engineering consultants anchor scope, ROI assumptions, and risks before you invest.
Cloud Data Platforms
We design and implement modern stacks on AWS, Azure, or GCP using Snowflake, BigQuery, Redshift, Databricks, or Synapse. Work includes VPC/VNet setup, networking, security baselines, role design, and IaC (Terraform) for repeatable deployments. We add a semantic/metrics layer so finance, ops, and product teams work from one version of the truth.
Data Integration & Pipelines
We build batch and streaming pipelines with tools like Airflow, dbt, Kafka, Spark, Fivetran, and REST/GraphQL connectors. Patterns include CDC from ERP/CRM, S3/GCS landing zones, bronze–silver–gold modeling, and unit/data quality tests. Pipelines ship with SLAs, retry logic, lineage, and alerting so teams can trust delivery windows.
ML Engineering & MLOps
We productionize models using MLflow, SageMaker, Vertex AI, or Azure ML with CI/CD, feature stores, and model registries. Services cover feature engineering at scale, inference endpoints, batch scoring, A/B rollout, and drift monitoring. You get reproducible training, auditable versions, and an ops playbook your team can run.
Analytics & Data Science
We enable BI and experimentation with Looker, Power BI, or Tableau on top of well-modeled marts. We add governed datasets, reusable metrics, and experiment/event schemas for product analytics. When needed, we support DS use cases—forecasting, segmentation, NLP—paired with business adoption plans, not just notebooks.
DataOps, Observability & FinOps
We introduce release pipelines, environment promotion, and automated tests for data changes. Observability covers freshness, volume, schema, and anomaly detection with on-call runbooks. FinOps practices include usage tagging, storage lifecycle rules, warehouse optimization, and rightsizing to keep unit economics healthy.
Governance & Security
We implement policies for access, retention, and quality across the stack. Controls include IAM, row/column security, tokenization, PII masking, encryption in transit/at rest, and secrets management. We align with GDPR/CCPA, HIPAA, SOX/FINRA needs and document data lineage so audits and incident reviews move faster.
Industries We Serve
- Retail & eCommerce
- Healthcare & Life Sciences
- Finance & Banking
- Logistics & Supply Chain
- Manufacturing
- Government & Public Sector
- Startups
- SaaS
- Telecommunications
- Education
Challenges We Solve
Most teams don’t lack tools; they lack a reliable path from raw data to trusted decisions. We eliminate bottlenecks that block insight and growth.
Want a clear plan to fix data bottlenecks?
Why Choose WiserBrand
You need a data engineering partner that ships real systems, not concepts — and strengthens your internal team with every milestone.
1
Outcome-first roadmaps
We tie backlogs to business KPIs, quantify impact vs. effort, and fix scope early. You get a 6–12 month plan with architecture options, costs, staffing, and risk controls — not vague promises.
2
Cloud-native, platform-agnostic
AWS, Azure, or GCP with Snowflake, BigQuery, Redshift, or Databricks—picked for fit, not fashion. Everything is codified with Terraform and CI/CD so environments are consistent and recoverable.
3
Production-grade operations
DataOps from day one: tests, lineage, freshness SLAs, on-call runbooks, and incident postmortems. We track MTTD/MTTR and reliability SLOs so stakeholders trust delivery windows.
4
FinOps at the сore
We design for cost from ingestion to serving: usage tagging, storage lifecycle rules, query tuning, autosuspend, and workload isolation. Expect visible unit economics and predictable bills.
5
Compliance and security
Access models, PII/PHI controls, masking, tokenization, and audit trails mapped to GDPR/CCPA, HIPAA, SOX/FINRA. We document decisions so audits and incident reviews move fast.
6
Enablement, not dependency
We ship docs, playbooks, and handoff sessions for your data team. From dbt conventions to ML model registries, we standardize patterns so your staff can run and extend what we build.
Our Experts Team Up With Major Players
Partnering with forward-thinking companies, we deliver digital solutions that empower businesses to reach new heights.
Our Workflow
A predictable delivery model that moves from assessment to production, then to ongoing optimization and enablement.
Discovery & Diagnostics
We interview stakeholders, audit current pipelines and warehouses, and map systems, data domains, and KPIs. Outputs include a risks/assumptions log, data product inventory, quality baselines, and effort ranges tied to business outcomes — framing where data engineering services create the most value.
Architecture & Roadmap
We compare warehouse vs. lakehouse options, select platform components (e.g., Snowflake/BigQuery/Databricks), and define security and cost controls. Deliverables: target architecture diagram, decision records, backlog with value/effort scoring, and a 6–12 month roadmap with milestones, budgets, and hiring needs.
Build & Integrate
We implement ingestion (batch/streaming, CDC), transform with dbt/Spark, and codify infra with Terraform and CI/CD. Pipelines ship with tests, lineage, SLAs, and alerting; governance adds access models, masking, and audit trails. BI models and semantic layers make metrics consistent across teams.
Validate & Launch
We backfill safely, test data parity, run UAT with business owners, and cut over by domain. Readiness gates include quality thresholds, cost checks, monitoring dashboards, and playbooks for incident response. Releases are reversible, observable, and sized to reduce risk.
Operate & Enable
We run SLOs for freshness and reliability, handle incidents, and tune workloads for cost. Quarterly reviews target high-ROI improvements; training and documentation transfer ownership to your team. As data engineering consultants, we leave you with patterns your staff can run and extend.
Client Success Stories
Explore how our services have helped businesses across industries solve complex challenges and achieve measurable results.
Frequently Asked Questions
A clear roadmap, target architecture, and a production-ready slice: one or two high-value pipelines (CDC or batch), basic governance (access, masking), a semantic/metrics layer for a core domain, and cost/quality baselines.
AWS, Azure, and GCP; warehouses/lakehouses like Snowflake, BigQuery, Redshift, and Databricks; orchestration with Airflow and cloud schedulers; transformation with dbt and Spark; streaming via Kafka/Kinesis/Pub/Sub; ML platforms such as SageMaker, Vertex AI, and Azure ML; BI with Looker, Power BI, and Tableau.
Typical ranges: PoC $30–75k, phased implementation $120–500k, and managed operations $10–40k/month. We quote by outcome and scope, not seat count. FinOps practices are included so spend stays predictable as usage grows — common for data engineering solutions at scale.
Co-delivery with your engineers and analysts, weekly demos, and documented runbooks. Our data engineering consultants leave you with IaC, CI/CD, testing conventions, and handoff sessions so your team can operate and extend the platform without external bottlenecks.