Inside your systems, standups, and delivery flow by week two. Full model ownership, metrics discipline and DataTheta support behind every dashboard.
A senior analytics engineer embedded in your team owns the models, metrics, semantic layers, documentation and governance needed to make business data trusted and decision-ready.
Transform raw warehouse data into tested, documented, reusable models that support reporting, analysis and operational decision-making.
dbt, SQLMesh, Coalesce, Dataform
Define consistent KPIs, reusable business logic and governed metrics layers so teams stop arguing over numbers.
LookML, dbt Semantic Layer, MetricFlow, Cube
Build executive dashboards, operational reporting and self-service analytics experiences with clear definitions and trusted data.
Power BI, Tableau, Looker, Sigma
Automated checks, source tests, model validation and alerting so reporting issues are caught before stakeholders notice.
Great Expectations, dbt tests, Soda, Monte Carlo
Create model documentation, metric definitions, lineage, ownership and governance workflows that make analytics easier to maintain.
dbt Docs, Atlan, Collibra, Confluence
Partner with business teams to translate questions into reliable data products, dashboards and repeatable analysis workflows.
Jira, Slack, Notion, Miro
To contributing
Dedicated to your team
Minimum engagement
Typical match time
DataTheta team behind them
Production experience
Sprint planning
Model build
Dashboarding
Governance
Ops and handover
Analytics Engineer II
3–5 years · Supervised delivery
Best for defined analytics work, dashboard builds, model updates and supporting a senior lead. Strong execution focus.
Senior Data Engineer
5–9 years · Independent ownership
Owns models, metrics and dashboards end to end. Makes design decisions, improves trust and raises analytics standards without needing hand-holding.
Principal / Staff Analytics Engineer
9+ years · Platform leadership
Sets technical direction for your analytics layer. Best for metrics governance, BI transformation, semantic architecture, or teams needing a technical anchor.
Tell us the stack and the gap
Share the BI stack, warehouse context and what they need to own. A 30-minute conversation, no forms.
We propose within 5 days
A named engineer from our bench, with background, experience and a short technical assessment relevant to your stack.
You decide
A technical interview runs your way. If the fit is not right, we rematch at no cost. No commitment until you say yes.
In your team by week two
Structured onboarding, committed analytics work and standups from week two.
See how embedded engineering capability improves pipelines, platforms, quality, and decision speed.
Embedded data engineering support helped unify POS, inventory, and promotion data into reliable pipelines for demand forecasting and planning.
34% forecast accuracy improvement
A governed data platform connected claims, provider, clinical, and member data to support reporting, risk scoring, and operational analytics.
3 weeks to 2 days reporting prep
Event-driven pipelines brought sensor and operational data together to support asset monitoring and early risk detection.
14-day advance failure prediction
Answers to common questions about embedding a senior analytics engineer through DataTheta.
We usually propose a suitable analytics engineer within 5 working days. After alignment, onboarding is structured so they can contribute meaningfully by week two.
Yes. The engineer is embedded into your team, standups, tools, and delivery rhythm. They work as a dedicated contributor, not a disconnected external resource.
Yes. DataTheta matches engineers based on your current warehouse, BI, transformation, metrics, governance and documentation stack. The goal is fast contribution without forcing unnecessary platform changes.
We rematch quickly if the engineer is not the right fit for your technical needs, stakeholder style, team culture, or delivery expectations. You should only continue when the match works.
Yes. Engagements can extend into long-term embedded support, analytics ownership, or expanded team capacity. Many clients start with one engineer and scale once value is proven.
BI stack, warehouse, team size, duration — give us the context. We’ll have a name for you within five working days.
Build reliable pipelines, data platforms, transformations and observability practices that make enterprise data production-ready.
Build and operate the cloud, infrastructure, DevOps and platform foundations your data and AI teams need to ship reliably.
Develop predictive models, experiments, forecasting systems and machine learning workflows that turn complex data into measurable outcomes.
DataTheta is an enterprise Data, Analytics, and AI consulting company that helps organizations build AI-ready data foundations through Data Engineering, Data Science, Business Intelligence, Data Warehousing, Generative AI, and On-Demand Experts.
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