The Data Build Company is a data and analytics consulting firm focused on quality & delivery. Our team of data builders specialize in providing best-practice, scalable solutions for our clients. Whether helping our clients to build totally new data platforms for their organization or helping to realize new data solutions on an existing technology stack. DBC is your partner to ensure the implementation sits on a solid foundation and will last.

Helping PIA Group Nederland in their mission to become the most digitalized accountancy firm for SMEs in the Netherlands! Data is a cornerstone of this digitalization journey, therefore building a cloud, data and analytics capability from the ground up. Setting forth the data foundation, technology, people and skillset required to enable the journey ahead. Establishing best practices and reusable building blocks from day one to enable data products to scale effectively with the business. Practice what we preach - hands-on development.

Help businesses bridge the strategy gap to adopt emerging technologies as a core component of their information management and analytics infrastructure. Experience in designing, building, and maintaining complex data-based systems by leveraging cloud services (e.g., AWS, Azure), a variety of data technologies, languages and visualization software. Offer expertise along the full data life cycle - not only guiding businesses through the creation, synthesis, and storage of information, but also identifying opportunities to leverage advanced analytics and artificial intelligence to answer the right questions. Experienced in building and deploying engineering and analytics solutions across a variety of complex problems. Experience from development to production - building maintainable solutions which scale.

Key enabler to guide Brenntag on their data mesh journey with the responsibility to define and implement solution architecture, including a) Responsibility to design, develop and maintain data ingestion, data processing and data producing pipelines as per best practices, b) Design and build reusable components, frameworks, and libraries at scale to support analytics products, c) Implement an automated and resilient orchestration solution using modern scheduling tools, d) Build CI / CD scripts to ensure high quality production pipelines, as well as improvement of a monitoring and automated alerting framework.Led the migration of the global data products infrastructure and workloads to new, more secured, AWS accounts. Collaborated across departments with Data Analytics, Core IT and Enterprise Architecture to help define boundaries of responsibility associated with the data mesh architecture. Responsible for defining the detailed migration plan inclusive all networking, resource mapping, and ingress/ egress dependencies on external teams. Responsible to help mature the data engineering capability of the Data & Analytics department. Led the creation of data contracts and SLAs with external departments and D&A consumers. Introduced data maturity improvements via standards on data validation, back-up and recovery, archiving and retention, product lifecycle management, error handling and correction, data security and access control. Cultivated a continuous learning environment within the team and Data Engineering Chapter through persistent development of new skills and sharing of knowledge through mentorship. Enabled and provided guidance to a team of up to 10.

Joined CarNext during the need for cost optimization and technology / data pipeline simplification. From Day 1, responsible for moving the development and maintenance of a data mesh technology stack with 2.5 years of development from five external Sr/Md Data Engineers to two newly hired internal Jr/Md Data Engineers within six weeks. Decreased the Data & Analytics AWS expenses by USD 30,500 per month, USD 366,000 per year by right-sizing AWS services, managing compute with spot instances, and moving to serverless services where applicable.Responsible for stabilizing the operations and standardizing processes and best-practices by implementing; a) Standardized ticket management process in JIRA, b) Project on-boarding and documentation standards to decrease time of implementation and foster knowledge sharing, c) Environment sync to test against changes at source, while testing apps and data before promotion to production, d) Within three months moved from consistently failing tests and pipelines to nearly always green daily ops.Lead D&A platform and product architecture discussions. Responsible for a) Scoping of project and technical execution across the data engineering team with side-by-side development, b) Simplification of current architecture, solution design and implementation of adjustments to ensure maintainability, c) Day-to-day decisions on data pipelines within current architecture and hands-on handling of issues and pipelines, d) Mentoring of the data engineering team and improvement towards best practicesAdopted, developed, and maintained technology stack across historically three teams later merged into one. Including IaC (Terraform, Ansible), data extraction/consumption (EMR, Lambda, Confluent Kafka), CI/CD (GitLab), storage (S3, Redshift, RDS), orchestration (Glue, Airflow -> Managed Airflow on AWS, dbt), ML (Sagemaker) and monitoring (AWS CloudWatch), services/deployment (Elastic Beanstalk).

Responsible for defining and implementing a data and analytics strategy to enable the growth and maturity of a centralized practice within the global organization. Doing so by embedding: Latest Technology – Operationalizing a new Data and Analytics platform hosted in AWS to enable IoT, AI / ML, or reporting / self-service analytics use cases. Responsible for the data and solution architecture (i.e., logical, physical, contextual) alignment across domains. Hands-on implementation of best-practices via infra-as-code, networking, user management, data extraction/consumption, CI/CD, storage, orchestration, distribution, and monitoring. Automation – Implementing maintainable, automated workflows to create systemic data flows and governed data entry to remove error-prone manual processes.Data Foundation – Expanding data assets across the enterprise / system landscape with value-driven projects aligned to our data and analytics strategy and company-goals. Perform technical assessments of new potential projects for the team in alignment across domains, at Excom, and Board level. Provide solutions to technical complexities or bottlenecks presented by the business (incl. integration across SAP ERP, CRM, technical definition / roadmap towards Industry 4.0 / OT-IT across manufacturing facilities, and various other systems. Up-skilling – Maturing the team and skillset, both technical and soft skills. Creating new job functions and career development paths. Growing the team while establishing knowledge sharing and hands-on training opportunities aside a value-based project funnel. Process Improvement – Introduced an Agile Framework within the team and on-boarded product owners / business process experts to new way of working.

In association with Schiphol’s ambition to become the world’s best digital airport, lead Schiphol’s Data Science CoE (i.e., Data Science and Engineering Lab) - a team of ~20 data scientists and data engineers - to deliver production-grade products to the business. As a team, leverage the latest technology stack to deliver ML and AI solutions as streaming or batch applications; providing descriptive and predictive solutions serving Customer Experience, Operations, and Smart Assets and Buildings value streams. Play a key role in steering and implementing the final architecture and tooling for Schiphol’s Enterprise Data Platform, with direct impact on technology stack, scalability, security, maintenance, and data governance. Responsible for setting best practices in the team. Input into the company-wide definition of hiring for data scientists, data engineers, and data analysts. Established new level assessment and review framework tied to career goals and progression for the team. Grew the team by ~50%. Manage internal and external data scientists and data engineers capacity and allocation to projects. Perform technical assessments of new potential projects for the team. Step in ad-hoc for technical support on work streams. Final determination of preferred tooling for the team. Together with Product Owner, prioritize incoming projects from the business in relation to expected value.

Tasked alongside the team to address the stabilization of the infrastructure and applications. Worked with the team to prioritize / simplify / standardize the technical specs of incoming work and the technologies deployed across Schiphol's data and analytics platform. Grew the team and function alongside the needs of the business.

Help businesses bridge the strategy gap to adopt emerging technologies as a core component of their information management and analytics infrastructure. Experience in designing, building, and maintaining complex data-based systems by leveraging cloud services (e.g., AWS, Azure), a variety of data technologies, languages and visualization software. Offer expertise along the full data life cycle - not only guiding businesses through the creation, synthesis, and storage of information, but also identifying opportunities to leverage advanced analytics and artificial intelligence to answer the right questions. Experienced in building and deploying machine learning applications across a variety of complex problems. Experience from development to production - building maintainable solutions which scale.

Data Science & Engineering Lead of client engagements, to drive quantitative and qualitative business insights through ML techniques and industry-specific domain knowledge. Engagements included customer segmentation assignments, demand forecasting, market dynamics, intelligent revenue management applications, alliance partnership alignment, and numerous ad-hoc analytics work streams across departmental domains (e.g., Network Planning, Revenue Management, Sales, Operations).

Managed and architected cost-effective, scalable analytics solutions leveraging a variety of data platforms (e.g., MS SQL Server, Hadoop, Spark, Hive, S3, Redshift), languages (e.g., SQL, Python, R, SAS, Scala, PySpark) and visualization software (e.g., Tableau, Power BI). Deployed and managed specialized cloud data management resources (AWS, Azure). Developed and maintained pipelines connecting data lake or data warehouse solutions. Managed advanced analytics design of global client workstreams to drive quantitative and qualitative business insights through descriptive, predictive, and prescriptive approaches. Applied ML / AI techniques (e.g., Supervised, Unsupervised, Deep Learning and Reinforcement Learning) and industry-specific domain knowledge. Created process design and internal business case development for advanced analytics products. Team won Accenture Data Science Hackathon in London for customer hyper-relevance. Interacted with Senior Management, Executive, and C-level to adopt methodology and/or steer well-supported business decision.

Performed data mining and visualization techniques via SAS/SQL/Tableau to identify operational trends and assign drivers (e.g., exploratory and regression analysis). Developed statistical models to forecast safety metric performance and KPI creation (e.g., time-series forecasting and smoothing techniques). Created and maintained automated reporting solutions using a variety of business intelligence tools (e.g., Teradata, DB2, Oracle Hyperion) and programming languages (e.g., SAS, SQL, Bash). Troubleshot automated scripting issues and proactively monitored their performance with an effort to increase efficiency. Managed and/or assisted with ad hoc projects to support corporate and divisional operations initiatives. Effectively communicated with Senior Management across divisions to streamline and validate workstreams.

Leveraged descriptive, exploratory, inferential, predictive, and causal statistical analyses to deliver business intelligence for natural gas and electric utility clients. Applied ETL techniques across various client datasets via SAS/SQL. Performed energy efficiency analysis via SAS to implement statistical modeling procedures. Evaluated demand-side efficiency measures via statistical sampling techniques. Generated client-focused (technical and non-technical) reports documenting model selection and interpreted analysis.

Analyzed thermochemical process yields and improvement. Generated monthly reports pertinent to California Energy Commission (CEC) grant requirements. Researched and analyzed industry trends and technology adoption to strengthen business plan and incentivize investing. Itemized avenues for market penetration and quantified distinct competitive advantage. Developed business relationships to establish equipment procurement and first-level process review. Leveraged modeled results to initiate technology development under company budget. Maintained ownership of work in fast-paced, results-oriented start-up atmosphere. Expanded interdisciplinary research and analysis abilities to accelerate company growth.

Executed projects that enabled top-tier energy value chain clients to make strategy decisions. Supply- and demand-side forecasts (i.e., ARIMA techniques) within industry/market constraints. Gathered energy market data on production, consumption and price to analyze trends and assign drivers. Managed large datasets and presented forecast results within project narrative. Established forecast of natural gas production, pipeline and LNG movements, and industry adoption in North America. Evaluated the U.S. power market to determine renewables adoption and coal-fired plant retirements. Delivered an overview of technology potentials over the 2020-2030-2050 time frame, as well as specified the consequences of these developments upon the required peak capacity, gas volumes and CO2 emissions for Netherlands residential sector. Assessed the impact of building attributes and energy efficiency measures on energy consumption.

Analyzed economic and environmental impacts to help steer strategic client decisions - specifically a detailed consideration of Western European energy demand/supply, district heat, and renewable technologies. In-depth analysis of distributed generation technologies (i.e. microCHP, solar PV, micro wind, fuel cells, energy storage, biomass, diesel gen-sets). Data collection and analysis to support client recommendation.

Established relationships with international financial and manufacturing firms in China and Mongolia to analyze global market and policy variance. Discussed avenues for business development. Identified macro and micro economic trends.