As the central data team increasingly becomes the bottleneck for data and analytics progress, more companies are starting to explore self-service options (#DataMesh). However, data teams typically fear self-service because how can they ensure that opening up access to data doesn't result in a disorganized and messy data platform (#DataMess). This is where analytics engineers come in. They are - often decentralized - data analysts who are familiar enough with basic data engineering best practices to ensure that data and analytics products are built robustly.

Key components

Our analytics engineering service offering includes the following key components:

  1. Data Transformation & Modeling: Our analytics engineers are experts in data transformations, such as pivoting, aggregating, and joining datasets to create clean, structured, and insightful information. We employ best practices in data modeling, such as star schemas, snowflake designs, and data vaults, to create data models that cater to your specific use case.
  2. Data Quality Testing & Monitoring: Ensuring the highest quality of data is a top priority for our analytics engineers. We perform comprehensive data quality testing, including null checks, data type checks, and expected value/range assessments. Additionally, we implement data quality monitoring processes to ensure your data remains accurate and up-to-date.
  3. Data Warehousing & Lakehouse Management: Our analytics engineers have extensive knowledge of data warehousing and lakehouse concepts, allowing us to design and maintain robust data storage solutions. We can deploy multi-skilled teams where data engineers and analytics engineers work closely together to understand the nuances of various data architectures and implement the right solution to meet your organization's requirements.
  4. Dashboarding & Reporting: We believe that effective data visualization is crucial for understanding and communicating insights. Our analytics engineers are skilled in designing and building custom dashboards and reports, utilizing the best visualization practices and tools to ensure your data is presented in a meaningful and actionable manner.
  5. Analytics Engineering Process: Our analytics engineers follow a systematic approach to problem-solving, which includes thorough business use case analysis, functional analysis, data modeling, and end-product design.
  6. Integration with Modern Data Stack: As our data & cloud engineers can employ the modern data stack to build a platform that fits your needs, our analytics engineers will ensure that this platform is used in the most optimal way. The modern data stack touches aspects like data ingestion, data transformation, data pipeline orchestration, data visualization and more. Our analytics engineers make sure every component integrates seamlessly within your environment.
  7. Training & Knowledge Transfer: We believe in empowering our customers to make the most of their data. Our analytics engineers provide training and support in advanced SQL, data modeling, and other essential analytics engineering skills. We also offer guidance on using various data tools and technologies, such as dbt, SQL, Python, and more.

By partnering with Dataroots, your organization gains access to a team of dedicated analytics engineering professionals who are committed to helping you achieve your data-driven goals. We bring our expertise, experience, and passion for data to the table, ensuring you receive the highest quality analytics engineering services available. Let us help you transform your data into actionable insights and drive your organization forward.

Let's chat

Want to know more?

Connect with us to discuss your ideas, address your questions, or brainstorm together. Over a cup of coffee or a video call, whatever works best.