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🎙️ Thomas in't Veld: CEO & Co-Founder at Tasman Analytics
In this episode of SQL Lingua Franca, Thomas shares actionable strategies for helping companies advance their data maturity. From moving beyond basic dashboards to building scalable data systems, he explains how businesses can use composable architectures and modern tools to gain actionable insights faster.Thomas discusses common challenges, such as balancing quick iterations with long-term data strategy and avoiding "project hell," where teams get stuck in endless feedback cycles. He also explores how the role of analysts is changing with automation, focusing on deeper business-driven analysis instead of routine reporting.Whether you're setting up your first data team or scaling your data capabilities, this episode offers practical advice for making data a true driver of business success.
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🎙️ Mariam Halfhide: Practice Lead Responsible AI at XebiaIn this episode of SQL Lingua Franca, Mariam Halfhide, a data strategy expert, shares practical insights on integrating responsible AI into business operations. She explains how organizations can define ethical principles, implement governance frameworks, and address challenges like AI bias and privacy risks. Mariam also discusses real-world examples, such as the misuse of biometric data and the potential dangers of deepfakes.Tune in to learn how businesses can align their AI strategies with core values, navigate regulatory requirements, and build multidisciplinary teams to manage risks effectively. Whether you're a tech professional or interested in the societal impact of AI, this episode offers actionable advice for using AI responsibly.
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🎙️ Ruben Huidekoper: Analytics Engineer at Xebia
In this episode of the SQL Lingua Franca podcast, guest Ruben Huidekoper shares his journey as an analytics engineer. He talks about the broad scope of the role, which involves translating business needs into technical data solutions, working on data modeling, and overseeing pipelines. Ruben emphasizes the versatility of the job, as it requires both strong technical skills and the ability to communicate effectively with stakeholders, making sure the data solutions align with business goals.
Ruben also discusses the key traits that can help someone thrive in this field, particularly the need for curiosity and a willingness to continuously learn new tools and technologies. He reflects on the evolving nature of analytics engineering, which often overlaps with other data roles, offering flexibility and growth for those in the profession. This episode is a great listen for anyone considering a career in data or looking to understand more about the role of analytics engineers.
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🎙️ Travis Dent: Data Engineer at Xebia
In this episode, Travis, an experienced professional in data engineering and DevOps, shares his journey from robotics to data analytics. The discussion covers the importance of DevOps practices in data projects, addressing challenges like long-running pipelines and changing data schemas. Travis emphasizes the cultural aspects of DevOps, including collaboration, automation, and continuous improvement, while also touching on different levels of DevOps knowledge for data professionals.The conversation explores how project management and clear vision contribute to successful data initiatives. Travis and the host discuss how the data field can learn from other industries like aviation and construction, particularly reliability and alerting. The episode concludes with practical recommendations for listeners looking to enhance their DevOps skills, including book suggestions and online resources, making it valuable for data professionals aiming to improve their operational practices.
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🎙️ Pádraic Slattery: Principal Analytics Engineer at Xebiadbt beyond the basics: https://github.com/pgoslatara/dbt-beyond-the-basicsIn this episode, Padraic shares his background, starting from mechanical engineering and transitioning into data analysis and data engineering roles. He dives into a GitHub repository he created called "dbt Beyond the Basics", showcasing CI/CD best practices for working with dbt.Padraic explains his Airflow-based approach to orchestrating data pipelines, including techniques to improve fault tolerance. He also shares his thoughts on potential debt feature requests and emphasizes the importance of building a strong data engineering culture. Finally, we discussed how the listeners can help improve your organization's data maturity in three months, focusing on onboarding, CI/CD practices, and team self-sufficiency.
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🎙️ Ksenia Iakovleva: IT Service Manager at flatexDEGIRO
Django Girls: https://djangogirls.org/en/amsterdam/
In this episode, Ksenia discusses her transition from journalism to data analysis in the Netherlands' financial sector. She delves into the challenges of working in a regulated industry, the importance of thoughtful KPI selection, and the balance between meeting regulatory requirements and maintaining meaningful metrics. Sena emphasizes the need for KPIs that accurately reflect process goals without pushing for unrealistic targets.
The conversation then shifts to Ksenia's involvement with Django Girls, an international non-profit introducing women to programming. She describes the free one-day Python workshops and offers advice for those entering or advancing in data roles, stressing the importance of hands-on projects. The episode concludes with an invitation for experienced Python developers to volunteer as Django Girls coaches.
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🎙️ Luuk Feitsma: Head of Recruitment
In this episode, Luuk shares his diverse professional journey, starting in the music industry as a producer and DJ before transitioning into the IT recruitment sector, where he now serves as the head of recruitment.The conversation covers a range of topics related to recruitment, including the unique challenges of hiring data professionals versus other roles, the current volatility in the tech job market, the importance of company culture and purpose in attracting talent, strategies for assessing "soft skills" during the hiring process, addressing bias and fairness in recruitment, and the impact of AI on recruitment workflows.
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🎙️ Daniel Herrera: Principal Developer Advocate at TeradataToddler: https://github.com/Teradata/toddlerIn this episode, we explore the global data landscape with Daniel Herrera, a developer advocate at Teradata. Daniel shares his unique journey from Guatemala to Taiwan and Europe, offering insights into how data practices and IT cultures differ across continents. The conversation covers the evolution of Taiwan's tech industry, Teradata's role in big data solutions, and practical advice on data modeling and understanding business needs.Throughout the discussion, Daniel provides valuable perspectives on transitioning from social sciences to a tech career, cultural nuances in data engineering, and the challenges and opportunities presented by AI and machine learning. He offers practical tips for identifying valuable use cases for generative AI in business and shares insights from his work on the 'toddler' library project for question generation.
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🎙️ Jovan Gligorevic: Data Engineer at Bitpanda
In this episode, we explore the productionization of dbt (data build tool) with our guest, Jovan Gligorevic. The conversation delves into strategies for effectively deploying dbt, including using software engineering best practices, CI/CD pipelines, and orchestration tools.
Jovan emphasizes the importance of balancing speed of development with adherence to best practices and data security requirements. He also offers practical advice on improving the dbt deployment process, highlighting how these improvements can increase productivity and data quality in analytics engineering teams. The discussion covers topics such as pre-commit hooks, testing strategies, and the integration of dbt with tools like Airflow and Cosmos for efficient orchestration.
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🎙️ Tony Zeljković: Data Engineer at Full scale bioinformatics
In this episode, we explore the developer experience in data engineering with our guest, Tony. The conversation delves into strategies for enhancing developer experience, including the use of dev containers, proper system design with loose coupling, and effective testing practices.
Tony emphasizes the significance of stakeholder management and understanding different perspectives in data projects. He also offers practical advice on improving the overall developer experience, highlighting how these improvements can increase productivity and value in data engineering teams.
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🎙️ Ricardo Granados Lopez: Analytics Engineer at Xebia
In this episode, we delve into the world of data careers and analytics engineering. Our guest, Ricardo Granados, shares his journey from medicine and industrial engineering to analytics.
The conversation explores the evolution of data roles, particularly the emergence of analytics engineering as a bridge between data engineering and data analysis.
We also touch on critical aspects of data work, including data modeling, the challenges of working with different data environments, and the importance of data quality and governance.
The discussion highlights the often-overlooked human aspects of data projects, the evolving nature of data platforms, and the importance of continuous learning and practical experience in data modeling and analytics.
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🎙️ Fanny Kassapian: Principal Consultant at Xebia
In this episode, we dive into the world of data transformations. Our guest, Fanny Kassapian, shares insights on creating efficient, scalable, and user-friendly data pipelines, drawing unexpected parallels with IKEA furniture assembly. We also explore key principles such as code readability, modularity, and the art of documentation, debunking common misconceptions along the way.
The conversation touches on often-overlooked aspects of data work, including the importance of naming conventions, the power of well-designed dashboards, and the challenges of replicating real-world scenarios in data education.
