Hi, I'm Laura.

Engineer at heart. Builder by habit. Data is my language.
I like building data processes that work.

I love figuring out how things work. Whether it was learning languages, mapping survey logic, or designing data flows between teams, I’ve always chased that moment when a complex system finally makes sense.

Data engineering is the first field that lets me live in that space every day. I get to build the structures that make information move — to design, optimize, and watch things click into place. The satisfaction is the same as it’s always been: understanding patterns deeply and turning them into something that works in the real world.

Long before I deployed my first Airflow DAG, I was running data pipelines — they just happened to involve humans, spreadsheets, and field surveys across seven projects. I’ve always loved solving the puzzle of messy, mismatched data and designing systems that bring order to chaos.

Learning Python and cloud tooling finally gave me the freedom to engineer the kind of systems I used to imagine: automated, scalable, and elegant. I think in patterns — whether in languages, logic, or data architecture — and that’s what makes data engineering so satisfying to me. It’s the same curiosity that drove me to learn foreign languages years ago; now it’s expressed in code.

Experienced data professional transitioning from analytics and nonprofit programs into Data Engineer with previously 10 years experience in data-related roles with overseas humanitarian projects & the judicial sector.

Tools I've worked with (or Things I can do?)

Integer eu ante ornare amet commetus vestibulum blandit integer in curae ac faucibus integer non. Adipiscing cubilia elementum integer lorem ipsum dolor sit amet.

  • Snowflake, Postgres, Trino, Neo4j
  • Airflow, Dagster, AWS Glue
  • Cloud providers: AWS, GCP
  • dbt, Databricks, Spark
  • Iceberg, Big data table design patterns
  • Data Modeling, Data Quality
  • CI/CD, Jenkins, DevOps automation
  • Python, SQL
  • Kubernetes, Docker, Terraform
  • Grafana, PowerBi, Prometheus

Data Engineering Projects

Designed and deployed multiple end-to-end data engineering and DevOps systems integrating Python, SQL, Airflow, dbt, Spark, Snowflake, Postgres, BigQuery, Docker, and AWS/GCP environments. Projects simulate real-world pipeline automation, orchestration, and data warehousing challenges using production-grade frameworks and cloud services.

Guardian API ETL Pipeline

Automated daily article extraction (2020–2025) from the Guardian API; used SpaCy for NLP entity analysis and loaded results into Snowflake. Deployed on Docker with CI/CD pipelines via GitHub Actions.

TOOLS: Python, SQL, Airflow, dbt, Snowflake, AWS, SpaCy, Streamlit

NGO Data Please – Synthetic Data Generator (Python Application)

Built Python app to generate realistic multi-sector nonprofit datasets (millions of rows) from user interface; Pushes data in S3 and GCS for ETL pipeline testing and schema validation using Pydantic & infrastructure.

  • User inputs preferences in CLI or in Flask Frontend
  • Datasets in six hard to find areas, realistic dates and categorical randomness to make ETL pipeline simulations fully possible
  • Generates set of datasets & summary analysis to preferred target: S3 bucket or GCS Storage Bucket, and/or a set for development in local folder
  • Many optimizations to scale its capability, such as to Parquet file format, million rows doable in local memory
  • Functional developer tools to automate dev processes like memory analysis, output analysis, automation scripts

TOOLS: Python, PyArrow, Snowflake, AWS S3, Airflow, Streamlit, Flask, GCS, Argparse, Pydantic, Automation scripting, Github Actions

DevOps Practitioner Projects (Tech with Nana)

Completed DevOps Practitioner projects to simulate production-grade infrastructure. Deployed containerized apps and monitored pipelines with Prometheus/Grafana dashboards and Terraform automation.

  • CI/CD: Jenkins, Kubernetes EKS AWS
  • Automation on servers: Terraform proj, Ansible, Python & BASH scripting, AWS APIs (EC2, EKS, IAM)
  • Observability: Prometheus, Grafana proj

TOOLS: AWS, Jenkins, Kubernetes, Docker, Terraform, Ansible, Nexus, Python, BASH, Prometheus, Grafana

Humanitarian Distributions – Databricks ETL Workflow, Synthetic Data Generator

Developed SQL & PySpark ETL workflows to produce Synthetic distributions data simulation; orchestrated transformations with Airflow and wrote outputs to Delta tables for downstream analytics in Databricks.

  • The first thing this project did
  • Second thing this project did
  • Third thing this project did

TOOLS: Databricks, Snowflake, AWS, Airflow, PySpark, Python, SQL

Certifications

Integer eu ante ornare amet commetus vestibulum blandit integer in curae ac faucibus integer non. Adipiscing cubilia elementum integer. Integer eu ante ornare amet commetus.

Certificate of Excellence, Data Engineering Bootcamp (Jan-Mar 2025) | DataExpert.io

Infrastructure Focus

Python, Sql, Airflow, Pipeline Management, Trino, Databricks, Iceberg, Tabular/Data Catalogues, Table Patterns, PySpark Testing, AWS Glue, Snowflake

Certificate of Excellence, Analytics Engineering Bootcamp (Oct-Dec 2024) | DataExpert.io

Python, Sql, Airflow, Pipeline Management, Trino, Snowflake, dbt, Analytics Table Patterns, PySpark Testing, AWS Glue

DevOps Practitioner Bootcamp Certification (2024 - 2025) | Tech with Nana

Integer eu ante ornare amet commetus vestibulum blandit integer in curae ac faucibus integer adipiscing ornare amet.

Contact Me

Reach out for more information. Open to relocation. Open to remote work or on-site.