Fund an Analytics Engineering team and drive it towards a streaming data world
Problem
Working with a large corporate client in the digital marketing space through a Toptal engagement, we contributed to founding an Analytics Engineering team and preparing it to deliver reliably. The challenge was broader than starting a few data pipelines: the new team needed development standards, reusable frameworks and a way to collaborate efficiently with platform engineering, enterprise data and business departments.The existing support model was reactive. Data consumers would discover incorrect datasets and then ask the engineering team to investigate. That pattern limited trust and made it difficult for the team to communicate what consumers should expect from each dataset.
Architecture and operating model
The working stack was based around Snowflake, DBT, Ataccama, Prefect, Kubernetes and GitHub. The target operating model connected version-controlled analytics development with orchestration, data quality profiling and monitoring:
flowchart LR
github[GitHub development workflow] --> dbt[DBT transformations]
prefect[Prefect orchestration] --> dbt
kubernetes[Kubernetes runtime] --> prefect
dbt --> snowflake[Snowflake data platform]
snowflake --> quality[Ataccama profiling and quality monitoring]
quality --> feedback[Alerts and engineering feedback]
snowflake --> consumers[Data consumers]
The architectural direction was to retain DBT as the transformation core while the organization moved from batch-oriented delivery toward streaming data. This preserved an established development interface for analytics engineers while allowing the surrounding platform and ingestion patterns to evolve.Data observability and profiling were treated as part of delivery rather than an after-the-fact support activity. The team adopted or built frameworks that could surface quality problems earlier, give engineers better visibility and make expectations clearer to consumers.
Contribution
We established the initial development guidelines and frameworks, helped kick off the first data initiatives and provided ongoing technical consultation to remove delivery roadblocks. A significant part of the engagement involved aligning the new team with platform engineering, enterprise data and business stakeholders so that ownership and dependencies did not remain implicit.We also helped shape a DBT-centered development process that could be reused beyond the immediate team. The emphasis was on repeatable engineering practices rather than project-specific scripts, allowing other data teams in the organization to adopt the same approach.
Outcomes
shifted quality management from consumers reporting incorrect datasets toward proactive monitoring and engineering alerts
improved visibility into data health and made dataset expectations clearer to consumers
established a DBT development process that was embraced by other data teams in the organization
supported the move from batch processing toward streaming data while maintaining DBT at the transformation core
