October saw us host our inaugural Women in Tech meetup in New York. The event was held at Third Republic’s NYC HQ and before we gathered to hear the inspiring and insightful talks from our fabulous speakers, everyone had the opportunity to network over some drinks and nibbles. 

Speaking at the event we had two experienced and inspirational female Salesforce professionals, both passionate about improving diversity in tech by sharing their experiences and empowering others. Karen Lee, Salesforce Practice Director at Saberpoint and Devin Spence, Salesforce Solution Architect at Co-Pilot.

Support is key

Karen kicked-off her talk by discussing some of the stats around Women in Tech. These are just a few of the points Karen made, and why Women in Tech is still something that needs to be worked on:

•  25% of computing jobs are held by women
•  There are more than 35% of women over the age of 35 still in junior positions
•  Just 5% of tech leadership positions are held by women

As part of her talk, Karen coved three key topics:

•  How to be an ally to women in tech
•  How to progress your career as a women in tech
•  How to improve equality of WIT

Karen emphasised the importance of men in tech (the 75%) supporting their female counterparts. As well as, anyone in more senior tech positions being generous to the future pipeline of talent. Teach them and and help them to achieve success too.

Finally, Karen opened the conversation to the floor inviting any questions and prompting further insights into the world of women in tech. 

Architecting a career I love

Devin has worked on multi-million dollar project for a range of companies and explained how she has achieved such success in the Salesforce ecosystem by remaining – Hungry, Humble, Smart.

She explained the importance of ‘showing up’ and being hungry to take on new challenges. Whilst also emphasising the importance of being smart and understanding the value that networking offers, saying “tech is something that you can learn, but it’s the people skills that can be difficult”.

All three aspects are about really getting out there and demonstrating your value in the competitive world of tech.

 

We’d like to thank everyone who attended the event, as well as our fantastic speakers Karen and Devin for contributing to such a successful first event. There were some really interesting conversations had and insights and experiences shared around women and diversity in tech. 

If you’re interested in speaking at our next Women in Tech NY event, please get in touch.

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In this interview, Benjamin Hanrieder, CTO at Engel & Völkers Technology, discusses the company’s digital transformation, software development practices, and the role of the Cloud Platform Team in ensuring reliable and secure infrastructure. Mat Roche (MR): Can you give us an overview of your background?  Benjamin Hanrieder (BH):  Hi Mat,

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Senior Software Tech Lead Managers
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React.js
Node.js
AWS
JavaScript
React
Native
PostgreSQL
Ruby on Rails
Java
Angular
Golang
PHP

React.js
Node.js
AWS
JavaScript
React
Native
PostgreSQL
Ruby on Rails
Java
Angular
Golang
PHP

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Cloud DW: Snowflake, BigQuery, Redshift ETL/ELT Tools: Airflow, dbt, Fivetran, Talend Big Data Frameworks: Spark, Hadoop, Kafka BI & Visualization: Tableau, Power BI, Looker ML Libraries: TensorFlow, PyTorch, Scikit MLOps Platforms: MLflow, Kubeflow, W&B Data Governance: Collibra, Alation Data Observability: Monte Carlo, Databand Data Lakes: Lake Formation, Delta Lake SQL/NoSQL DBs: PostgreSQL, MongoDB Real-time Analytics DBs: Apache Pinot, Druid Gen

Cloud DW: Snowflake, BigQuery, Redshift ETL/ELT Tools: Airflow, dbt, Fivetran, Talend Big Data Frameworks: Spark, Hadoop, Kafka BI & Visualization: Tableau, Power BI, Looker ML Libraries: TensorFlow, PyTorch, Scikit MLOps Platforms: MLflow, Kubeflow, W&B Data Governance: Collibra, Alation Data Observability: Monte Carlo, Databand Data Lakes: Lake Formation, Delta Lake SQL/NoSQL DBs: PostgreSQL, MongoDB Real-time Analytics DBs: Apache Pinot, Druid Gen

Sample of Skills Delivered

Programming in Python, R, and SQL
Python
Data Modeling Statistical
Analysis ETL/ELT
Pipeline Development
Database Design & Administration
Machine Learning Fundamentals
Cloud Data Warehousing
Data Ethics & Compliance
Data Security
Data Visualization & Storytelling
Real-Time Data Streaming

Programming in Python, R, and SQL
Python
Data Modeling Statistical
Analysis ETL/ELT
Pipeline Development
Database Design & Administration
Machine Learning Fundamentals
Cloud Data Warehousing
Data Ethics & Compliance
Data Security
Data Visualization & Storytelling
Real-Time Data Streaming

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LLMs: Open AI, Anthropic, Llama, Gemini ML Platforms: SageMaker, Vertex AI, Azure ML ML Libraries: scikit-learn, XGBoost, LightGBM DL Frameworks: TensorFlow, PyTorch, Keras LLM Frameworks: LangChain, LlamaIndex Vector Databases: Pinecone, Weaviate, Milvus Data Science Notebooks: Jupyter, Colab, Kaggle Data Visualization Libraries: Matplotlib, Seaborn NLP Libraries: Hugging Face Transformers CV Libraries: OpenCV, Pillow, Albumentations MLOps Tools: MLflow, Kubeflow, W&B

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Generative AI Development
Machine Learning Algorithms
Deep Learning Architectures
NLP and NLG
Computer Vision
Data & Feature Engineering
MLOps & Model Deployment
Statistical Analysis & Hypothesis Testing
Python and R Programming Expertise
Model Evaluation & Performance Tuning
AI Governance & Compliance
Autonomous AI Agent Engineering
Building Agentic AI Applications

Generative AI Development
Machine Learning Algorithms
Deep Learning Architectures
NLP and NLG
Computer Vision
Data & Feature Engineering
MLOps & Model Deployment
Statistical Analysis & Hypothesis Testing
Python and R Programming Expertise
Model Evaluation & Performance Tuning
AI Governance & Compliance
Autonomous AI Agent Engineering
Building Agentic AI Applications