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Mati Data

I help teams design, build and govern data platforms they can trust.

Senior Data Engineer at Kifiya Financial Technology in Addis Ababa, running lakehouse, CDC and warehouse platforms for digital lending since 2024. I take data work from architecture and governance to production pipelines and analytics. AI engineering is my second discipline.

How I can help

A pipeline I designed, with public code

Select a stage to see the decision behind it and the evidence. Arrow keys move between stages; Enter opens the case study.

  • Data flow
  • Control plane

  3. Debezium reads the write-ahead log through pgoutput over an explicit publication and writes flattened events to Redpanda, with the operation, the LSN and a deleted flag beside each row. The natural key is the primary key, so a delete still names the business entity.

    Evidence

    about 3 seconds from PostgreSQL commit to a queryable ClickHouse row

    • Debezium
    • Redpanda
    • pgoutput

    Read the case study: wb-cdc-analytics: PostgreSQL to ClickHouse CDC in one command

Data architecture and engineering, end to end

I work across the whole data lifecycle: deciding what the platform should look like, building it, keeping it governed, and making it useful to analysts and models. Most of my work is data architecture and engineering. AI engineering is my second discipline, and it always sits on top of good data.

Explore the services

Evidence you can check

Figures from my public code and research. Production figures from my employer work are on the CV, and the full case studies are shared on request.

58
dbt tests and 59 unit tests green in CI in my public CDC pipeline
10
Prometheus alert rules, each with a runbook and a promtool test
306
Ethiopic characters recognised by my Amharic OCR app
4-bit
costs about 3 points, 3-bit about 20 or more, in my Amharic SLM quantization study

Platform credentials

Career tracks completed alongside the production work: the warehouse and lakehouse platforms most data teams run on.

All credentials

  • Databricks

    Associate Data Engineer in Databricks

    DataCamp career track, 2026

  • Snowflake

    Associate Data Engineer in Snowflake

    DataCamp career track, 2026

    View the statement

  • SQL and PostgreSQL

    Associate Data Engineer in SQL

    DataCamp career track, 2026

    View the statement

Data only helps when people trust it. Here is how I make that happen.

From the first architecture decision to the dashboard your team opens every morning: designed, built, governed, and handed over so it keeps working.

  • Data architecture

    Target-state designs for lakehouses and warehouses that fit the sources, the team and the budget you actually have, written down as decisions you can defend later.

  • Data-intensive systems design

    Designs that hold up under retries, replays and partial failure, reasoned from how storage engines, logs, replication and transactions actually behave.

  • Data engineering

    Production pipelines that fail loudly instead of quietly: orchestrated, tested, idempotent and cheap to rerun.

  • Data governance and quality

    Governance built into the platform rather than added in meetings: contracts at model boundaries, lineage on every run, and access rules enforced at query time.

  • Analytics engineering and BI

    Gold layers that answer the business question in one table, so analysts and dashboards stop joining raw data at query time.

  • Enablement and training

    Leaving a team able to run what we built: clear handovers between data science and data engineering, documentation that answers the 2 a.m. question, and teaching that sticks.

  • Second discipline

    AI and ML on your data

    Putting models on top of a platform that can feed them, from feature stores to retrieval over your own documents.

Industry focus

Fintech data, from the inside

My production career has been inside fintech: digital lending, credit scoring, and the data that risk teams, finance teams and partner institutions depend on every day. I know what a repayment schedule, a days-past-due bucket and a credit decision need from a data platform, and what goes wrong when the platform cannot supply it.

See the fintech page

Selected work, and how it was built

Case studies written as constraints, decisions and outcomes. My public projects are open to everyone; the production case studies from my employer work are shared on request.

See all the case studies

What colleagues say

I've had the pleasure of working closely with Matiwos, a Data Engineer at Kifiya, and his growth has been exceptional. He learns new technologies with impressive speed, consistently applies them effectively, and brings sharp problem-solving skills to every task. His focus, professionalism, and ability to deliver high-quality results make him stand out. Matiwos is an outstanding data engineer with tremendous potential, and I highly recommend him.
Natnael Argaw WondimuGroup CTO, Kifiya Financial Technology PLC
I've had the pleasure of working with Matiwos and want to commend him for his exceptional work on our team. He demonstrated outstanding technical proficiency in managing and optimizing data pipelines, ensuring data integrity, and implementing efficient solutions that significantly improved our data processing capabilities. Matiwos is quick to take action, consistently responding to challenges and tasks with enthusiasm and a solutions-oriented mindset. His ability to communicate effectively coupled with strong problem-solving skills, made him an invaluable asset. I am confident that he will bring the same dedication and expertise to any future role.
Maelaf E. TegegnSenior Data Engineer, Forecasa
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Get to know me better

  • About me

    How I got here, how I work, and the people I have worked with.

    Read about me

  • The CV

    General, data engineering and AI versions, each ready to print as a PDF.

    Open the CV

  • Writing

    Notes on retrieval, forecasting, data warehousing and Amharic language models.

    Read the writing