I build pipelines that run when nobody’s watching.

with DAG(
    dag_id="due_fx_elt",
    schedule="15 * * * *",
    catchup=False,
    default_args={"retries": 3, "retry_delay": timedelta(minutes=5)},
) as dag:

    extract = PostgresToGCSOperator(
        task_id="extract_transactions",
        sql=INCREMENTAL_SQL,  # bounded by data_interval_start
        bucket=RAW_BUCKET,
        export_format="parquet",
    )

    load = GCSToBigQueryOperator(
        task_id="load_staging",
        write_disposition="WRITE_TRUNCATE",
        destination_project_dataset_table=f"{PROJECT}.staging.transactions",
    )

    merge = BigQueryInsertJobOperator(
        task_id="merge_core",
        configuration={"query": {"query": MERGE_SQL, "useLegacySql": False}},
    )

    transform = BashOperator(task_id="dbt_run", bash_command="dbt build --target prod")

    extract >> load >> merge >> transform

Abuja, NG WAT / UTC+1

I came to data engineering from statistics, which is why I care less about the dashboard and more about whether the number behind it can be trusted at 3am. I build pipelines that are boring on purpose: idempotent, observable, and easy for the next person to read.

I’m in Abuja and work with teams anywhere.

Currently: News Editor at MyTradingLand · Community Moderator at Cantor8

B.Sc. Statistics — University of Nigeria, Nsukka

Professional Diploma, Data Engineering — AltSchool Africa

DataCamp Professional Data Engineer — certified