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DataDost AI

Service catalog

Focused data engineering and analytics capabilities.

Use this catalog as a scoped reference for data pipelines, warehouses, dashboards, automation, data quality, and fractional data-team support. Broad adjacent capabilities are only used when they support the accepted data-system scope.

Focused data engineering and analytics capabilities.AI-ready data foundationSourcescontractsPipelinestestsWarehousemodelsMetricsownersAI / dashboardsdecisionsEvery visible output is tied back to source contracts, quality checks, named owners, and a handover trail.

Category DA

Data infrastructure and pipelines

ETL/ELT pipeline development, orchestration, dbt models, ingestion frameworks, pipeline monitoring, lineage, and the core infrastructure layer that moves data from source to warehouse reliably.

Category DB

Data warehousing and storage

Cloud data warehouse setup and optimization across BigQuery, Snowflake, Redshift, and Databricks - plus data lake, lakehouse, vector, and time-series storage architecture.

Category DC

Analytics and business intelligence

Executive, sales, finance, operations, and product dashboards built in Looker, Tableau, Power BI, Metabase, or Superset - with agreed metric definitions, refresh cadence, and self-serve access.

Category DD

AI and LLM engineering

LLM integration, RAG systems, vector embeddings, document AI, intelligent search, AI copilots, fine-tuning pipelines, multi-agent orchestration, and production-grade AI features built into your product or operations.

Category DE

Machine learning and data science

Churn prediction, demand forecasting, segmentation, recommendation engines, anomaly detection, NLP pipelines, A/B test analysis, and applied ML models built for business use cases - not research.

Category DF

MLOps and model operations

End-to-end ML pipeline setup, model deployment and serving, drift monitoring, feature stores, experiment tracking, CI/CD for ML, model versioning, and production reliability for AI systems.

Category DG

Real-time and streaming analytics

Apache Kafka, AWS Kinesis, Google Pub/Sub, Flink, and Spark Streaming pipelines for event-driven architectures, real-time dashboards, live anomaly detection, and IoT data ingestion.

Category DH

Data governance and quality

Data quality frameworks, data contracts, SLA monitoring, master data management, PII detection and masking, GDPR/CCPA compliance engineering, audit logging, and data observability tooling.

Category DI

Data integration and connectors

Fivetran, Airbyte, and custom API connectors to pull data from Salesforce, HubSpot, Shopify, Stripe, Google Ads, Meta Ads, Mixpanel, and any other source into your warehouse.

Category DJ

Product and growth analytics

Mixpanel, Amplitude, and PostHog setup - user behavior tracking, funnel analysis, retention and cohort reporting, feature adoption dashboards, and the growth metrics stack for product-led companies.

Category DK

Revenue and financial analytics

SaaS metrics dashboards (MRR, ARR, churn, LTV, CAC), burn rate and runway visibility, unit economics modeling, revenue forecasting, FP&A dashboards, and investor-ready data room analytics.

Category DL

Fractional data team

On-retainer data engineers, data analysts, ML engineers, and Head of Data - scoped by hour, week, or month. The human layer that operates, maintains, and evolves your data infrastructure.

Category DM

Data strategy and advisory

Data maturity assessments, stack selection, architecture reviews, AI readiness audits, build-vs-buy analysis, data team design, vendor evaluation, and data due diligence for M&A or investor review.

Working surface

Map the first reliable data path.

Bring the source exports, dashboards, and decisions that currently create friction. We return a scoped pilot path with owners, evidence, and handover expectations.

Focused data engineering and analytics capabilities. | DataDost AI