Warehouse setup, end to end
Pick the right warehouse for your data shape, set up Airbyte or Fivetran ingestion, design the dbt project, ship dashboards. You walk away with a stack that won't rot in 6 months.
Your data and AI stack costs you
I make it pay you back
I'm Owais. US-based senior analytics engineer, 6+ years at HP, Microsoft, and growth-stage startups. I build the warehouse, the pipelines, the dbt models, the dashboards, and the AI layer your competitors pay three people for.
-- Atomic event stream: app + web + Segment, unified {{ config(materialized='incremental', unique_key='event_id') }} with events as ( select * from {{ ref('int_events') }} {% if is_incremental() %} where occurred_at > (select max(occurred_at) from {{ this }}) {% endif %} ), users as ( select * from {{ ref('dim_users') }} ) select e.event_id, e.occurred_at, e.event_name, e.account_id, u.user_id, u.plan_tier, e.props from events e left join users u using (account_id)
Past clients and employers
How the work gets done
One person doing what used to take three
A startup used to need three people for this: someone to move the data, someone to model it, someone to build the dashboards. I've done all three jobs at once for 10+ clients.
Where the time goes
Three people means a ticket, a hand-off, and a wait before every step, because the next person needs the context the last one had. With one person there is nothing to hand off, so the next step starts the same day. I still write the spec and I still review the work before you see it. What you skip is the waiting in between.
AI can code, but it doesn't know your business
It doesn't know that revenue means invoiced and not booked at your company, or that the Stripe export double counts refunds. I find those things out on the first call.
Somebody still has to check the numbers
Everybody wants to know the AI got it right, and that takes a person who knows what the number should be. AI writes code fast and it makes mistakes with the same confidence. I check every model against the source data and I read every line before it merges. Nothing reaches you that I haven't signed off on.
What a year of data engineering and reporting costs
What I delivered in one year for a real client (2025)
ThoughtSpot dashboards and liveboards
172 hrs
Metric definitions and analyses
104 hrs
Reports, exports, and data requests
152 hrs
Pipelines and integrations from 12 source systems, Airbyte, Fivetran, and custom Python
313 hrs
Data modeling in dbt, 79 models
154 hrs
Workflow automation in n8n
214 hrs
AI agent and LLM features
93 hrs
Warehouse, infrastructure, debugging, and QA
57 hrs
Meetings, documentation, and project management
208 hrs
12 months1,467 hrs
Hiring a data engineer and a data analyst for that year
Data engineer · $141,991Median posted salary, last 30 daysData analyst · $111,000Median posted salary, last 30 daysBenefits · $75,897Health, 401(k) match, payroll tax, PTO
$330,000 a year
Hiring me for that year (2025)
Just me · $87,631Pay-as-you-go, billed only for hours worked
73% cost savings
Source: real job postings from the last 30 days, benefits at 30%, and my invoicing ledger.
How I work in 2026 as a forward deployed analytics engineer
2025
- Meetings and documentation14.2%
- Pipelines and integrations21.3%
- Data modeling in dbt10.5%
- Dashboards, reports, and data requests29.2%
- Workflow automation14.6%
- AI features6.3%
- Infrastructure, debugging, and QA3.9%
2026
- Requirements sessions with your team8.7%
- Meetings and documentation14.2%
- Pipelines and integrations13.6%
- Data modeling in dbt5.8%
- Dashboards, reports, and data requests12.3%
- Workflow automation8.9%
- Automating repeatable work14.0%
- Building AI agent context layers18.7%
- Infrastructure, debugging, and QA3.9%
Coding got cheap. Everyone has Claude Code and Codex now. I spent 430 hours in 2025 building dashboards and answering data requests one at a time. That work takes a fraction of the time now, and knowing what to build is still the expensive part. So I sit inside your team, and 600 hours of that year go into automation and AI agent context layers your team runs without me.
Source: 2025 from my invoicing ledger. 2026 is my estimate.
Common problems
Been dealing with any of these?
- Someone connected an AI tool to the warehouse and nobody can say which answers are right
- Finance has one revenue number and the dashboard has another
- A contractor left a dbt project nobody on the team understands
- The ingestion bill grows with row count, not with the business
- SOC-2 or HIPAA is holding up a pipeline you need
- Somebody re-types data from one system into another every week
- The board deck is due Friday and the numbers live in six places
- More tables than anyone has counted, and four people keeping four spreadsheets about them
- Dashboards exist and nobody opens them
- There is no data hire, so reporting falls on whoever is closest
What I do
Seven services that cover everything from raw sources to AI-ready data
I've worked hands-on with all the tools below, and I know what each one costs, where it stops scaling, and which ones fit together.
dbt + semantic layer
A clean, version-controlled, tested dbt project on top of your sources. This is the foundation BI, AI, and exec reporting all sit on. Get it wrong and everything downstream is shaky.
AI & agent layer
MCP servers, Snowflake Cortex, LLM Q&A grounded in your warehouse. Agents and humans hit the same trusted models so the answers don't drift.
Workflow automation
The work someone on your team re-types between systems every week, moved into the tools you already pay for. Documented so your own people can change it after I'm gone.
Power BI / Metabase implementation
Stood up, themed, connected to your models. 6–10 dashboards your team will actually open, with permissioning and embed-ready setup if you need to share with clients.
Migration & rescue work
ClickHouse to Snowflake, Postgres to Snowflake, Trello to ClickUp, undocumented dbt to tested dbt. I've done the migrations everyone's afraid to do and I know where the bodies are buried.
CRM data integration
HubSpot, Salesforce, and billing joined to product usage in the warehouse, so revenue and churn come off one set of numbers. Set up by someone who has worked inside these tools, not just pulled from them.
Selected work
Past work with measurable results
0×
Cheaper monthly ingestion
Hit SOC-2 encryption requirements without paying the Fivetran tax.
Built the full data platform solo — 68 dbt models across 13 sources into Redshift, KPIs defined from zero. Hit SOC-2 by routing Airbyte through a read-only Postgres replica with source-side PII obfuscation. Shipped a 4,000-line MCP server so AI agents could query the warehouse securely.
Ingestion cost$1,000 → $40/mo
Models in prod68 across 13 sources
EnterpriseParamount, Discount Tire, Gopuff
dbtRedshiftAirbytePrefectn8nMCPThoughtSpot
−0%
Warehouse cost reduction
Killed 90% of the dbt model sprawl and rebuilt the warehouse at 40% lower spend.
Inherited 500 undocumented tables across PostgreSQL, Mixpanel, GA4, HubSpot, Stripe, and Mailerlite. Consolidated into 50 dbt models with column-level lineage. Tuned ClickHouse partitioning, TTLs, and shard layout for sub-second queries at lower spend. Later migrated the whole project to Snowflake.
Warehouse cost−40%
Models in prod500 → 50
Sources6 consolidated
ClickHousedbtSnowflakeAirbyteMetabase
−0%
Storage cost reduction
Petabyte-scale telemetry with schema drift detection in front of every new device model.
Bronze/Silver/Gold PySpark pipelines on Airflow processing telemetry from millions of HP laptops and printers. Built schema drift detection and full pipeline QC — schema, type, range, and duplicate checks — after a data quality incident leaked bad records downstream. Cut storage by moving hot data to Parquet, and rewrote GZIP decompression to stream in chunks after large payloads blew up executor memory.
Storage cost−30%
ScaleBillions of events/day
ReliabilitySchema drift + pipeline QC
PySparkAirflowSnowflakeParquetTableau
Hear what my clients have to say
Client Testimonials
"Incredible data engineer. Worked on every data problem we had (AI tech startup), built out BI tooling and infrastructure, many automations and workflows that allow our business to operate so much better and be a true data-driven company."
Elliot MansionHead of Marketing | Upwage
"Owais supercharged our data infrastructure by organizing our tables into an analyst-friendly structure using dbt. He also built custom Python integrations, leveraging the latest advancements in AI.
Most importantly, Owais is a great communicator. He worked closely with all departments to understand their needs and delivered clear, effective solutions. When his contract ended, he left our team in a great place — with robust alerting systems and both written and video documentation.
I would 100% rehire him and highly recommend him to anyone looking for a strong data partner."
Justin HardinCTO & Co-founder | Climatebase
"Working with Owais was an absolute delight. He approached every problem with a curious attitude and picked up new skills and the nuances of our business quickly. He was always hungry for more and willing to collaborate with others on the team. Would highly recommend and hire again."
Peter KasparCEO | Terpli
Full reviews on Upwork
How I work
Fixed scope, and no surprise invoices
PHASE 01
Email + 30 min call
Tell me your stack, your bills, and what you're trying to ship. Free, no pitch.
You get: a one-pager of where I'd start.
PHASE 02
Audit & scope
1–2 weeks with read-only access. I map what you have and write a fixed-scope proposal.
You get: proposal + architecture diagram.
PHASE 03
Build, in sprints at your pace
You see PRs, review dashboards as they ship, never get surprised on a Friday.
You get: shipped pipelines and dashboards.
PHASE 04
Fire me
The goal of every project is that you can. You get docs and runbooks a new analyst can run the stack from, and a reporting setup your team can serve itself from. 30 days of bug-fix support after hand-off, and I'm an email away after that.
You get: docs, runbooks, a team that can run it without me.
If you'd rather keep me on retainer than scope a project, I do that too. You pick the number of hours per week and it renews monthly.
Owais MumtazFOUNDER · STREAMLINE ANALYTICS
BasedSeattle, WA
Experience6+ years
EducationBA Information Systems, UTD
AvailableYes
Personal projects
mumtazm1/self-hosted-analytics
One docker-compose that runs Postgres, n8n, Prefect, and Metabase. I keep my personal data in it, and the live numbers on this page come out of it.
Dockermumtazm1/echobox
A desktop app that records my meetings off the calendar and writes a transcript with each speaker labeled. Everything runs locally.
Python · Electronmumtazm1/tiro
Voice typing for KDE. I press a shortcut and talk, and the text lands on my clipboard. Transcription runs locally on Parakeet or Whisper.
Python · KDEmumtazm1/claw-whisperer
OpenClaw has a voice wake on Mac and none on Linux, so I wrote one. You say the wake word, and the reply comes back spoken.
Pythonmumtazm1/claude-code-skills
The skills I run inside Claude Code, published as a plugin so anyone can install them. One of them turns a private repo public without breaking the copy you still use.
Claude Code pluginAll public on GitHub.
About
Senior analytics engineer, 6+ years building data platforms for people and for AI agents
My track record
4,777+
hours supporting client teams, and counting
25+
projects shipped
14
clients since 2023
5.0
average client rating
★★★★★
58 min
median reply time to client messages
Live from my invoicing ledger and client threads. Updated Nov 3, 2026.
I've spent six years on the inside of data systems. At HP I built PySpark pipelines processing billions of device telemetry events. At Microsoft I was on the side that gets paged when Fortune 500 ETL breaks on Databricks and Azure. At Climatebase and Upwage I architected the data foundation from scratch.
I specialize in dbt, Snowflake, ClickHouse, and the semantic-layer-for-AI-agents stack. The same patterns teams hire three engineers for, delivered by one person who's accountable for the outcome.
I started Streamline because most small and growth-stage teams don't need an agency or a full data team. They need someone who's already done the hard parts, knows which tradeoffs matter, and can ship.
If your data or AI stack is costing more than it's returning, or you're stuck somewhere between Sheets and a real warehouse, email me.
Metabase AI Hackathon 2026 winner
I built an MCP server so Claude could read my fitness data in Metabase and write its advice back onto the dashboard as a chart. Metabase picked it as a winner and wrote about it on their blog. Check out my winning submission below.
Let's talk
If your data or AI stack is costing more than it's returning, let's fix it
30 minutes, no pitch deck. I'll tell you whether I can help, where the easy wins are, and what I'd scope first. If I'm not the right fit, I'll tell you who is.
I work best with teams that
- Have outgrown spreadsheets but don't have a real warehouse yet
- Are spending more on cloud data than they can explain on a slide
- Run a clinic and want the back office automated with PHI handled properly
- Want one accountable person, not another vendor relationship
- Are exploring AI on top of their data and need the foundation right
- Have shipped an AI feature and can't say how many customers are coming back to it
- Care about adoption as much as architecture