Leading a multidisciplinary data platform team at 99designs and fostering a data-driven culture across the organisation. This includes DataOps, developing self-service data products & feature stores, scaling the modern data stack including ingestion & curation pipelines (ELT) and collaborating with a diverse group of stakeholders (engineering, product, design, marketing and support) to turn business needs into actionable items.

Skills include:

• Architect, build and scale our data engineering platform and self-service data products (data lake, Kinesis, dbt, looker, mode etc).
• Data ingestion pipelines: collecting and processing data across the entire stack, from client-side event logging to ELTs, ensuring pipelines are reliable, efficient, tested, & maintained (segment, airbyte & debezium, kinesis etc).
• Develop, maintain and deploy ML applications, from feature engineering to feedback systems. Identifying opportunities in personalisation, recommendations and matching engines (segment personas, aws personalize, fastai, mlflow, dagster etc).

Prior to this, I spent a few years at pymetrics initially as a Data Scientist, working on neuroscience games and bias-free AI, building custom cross-validated ML models and helped develop an open source package, AuditAI, a python package that promotes fairness-aware machine learning algorithms. I later began focusing on enhancing our existing infrastructure and data tools that empowered the broader organization. This inspired me to transition into data engineering to develop ETL pipelines connecting various data sources through standalone microservices using Kubernetes, Docker, Airflow, DBT, Mode, Stitch and various AWS products as well as establishing security and data quality control measures through unit testing & monitoring via Newrelic.

Continuously studying and engaging in SoTA practices in Deep Learning and Machine Learning through online courses such as fast.ai and MOOC platforms (edX, MIT-OCW, etc). More recently, my experience has extended to PyTorch to solve various problems in NLP and Image Classification. Overall, my success in this area is emphasized through a deep understanding of fundamental mathematical and engineering principles, advanced statistical analysis as well as several programming languages.

Graduated from the University of New South Wales in June 2015, completing a double degree in Engineering and Commerce and receiving a first class honors in Engineering including a High Distinction in my thesis. Throughout this degree, I developed proficiencies in a diverse range of disciplines including computer science, mathematics, econometrics, statistical analysis and engineering principles. My thesis utilized a range of skills in predictive modeling, fractal theory, programming and statistical analysis.

Skills

• DevOps: Kubernetes - Docker - Databricks - Dagster - CDK - CloudFormation - Terraform - NewRelic & DataDog
• ELT (ETL) - Airbyte - Airflow - dbt
• Spark - Dask - Parquet
• Databases (Snowflake, Redshift, PostgresSQL, Cassandra, MongoDB, DynamoDB, MySQL)
• Segment - Stitch - FiveTran - Mode - Looker - Domo - Iterable - Indicative
• Queues - Celery, RabbitMQ, Redis, Memcached, Huey, Papermill
• AWS (EC2, EMR, Redshift, Spectrum, Kinesis (Firehose), DMS, Lambda, Athena, Glue, Sagemaker, Personalize)
• Deep Learning - TensorFlow, PyTorch - CV, NLP
• Machine Learning (XGBoost, SVM, Logistic, Decision Tree, Random Forest)
• Python (SciPy, Scikit-Learn, Dask, Pandas, NumPy, Matplotlib, Seaborn) - Django - SQLAlchemy - FastAPI - Flask - Quart - Gunicorn - Hypercorn - Jupyter
• Go - Twirp (RPC x Protobuf) - GraphQL
• GCP - AWS - Azure
• Statistics - Calculus - Algebra
• Git

Projects

• Audit AI
• MIT Missing Semester
• Esky
• Deep Learning: Computer Vision Projects
• Deep Learning: NLP Projects
• Data Engineering Capstone Project
• Implementation of a Kohonen Self Organizing Map (SOM)
• Exploratory Data Analysis in NYC SAT Scores
• Machine Learning: Melbourne Housing Price Predictions
• Determining The Flow Beneath an Embankment Using Laplace