How can you identify different data engineering roles?

Good data quality starts at the source - investing in solid data extraction and validation upfront prevents issues downstream. Building flexibility into ingestion systems is key to handle evolving data sources and formats like new JSON schemas. Contextual awareness of data sources helps validate correctness - know expected ranges, relationships between fields, etc.

A data engineer is responsible to bring data from source to end user. Until and unless there is a team of multiple data engineers working on a single project, there will not be such a distinction between the different roles of a data engineer. While work functions could be divided among team members, a data engineer is ecpected to know all functions.

In over a decade, this is the first time I read something called a "Data Source Engineer". Mostly Data Engineers do this initial recee activity before designing the extraction strategy -push or pull! Sometime Data analysts also do this initial analysis during POCs.

Data engineering is all about how we can prepare the data that is ready for the other people to consume especially data scientists to draw the business decisions from data. How ever the role and responsibilities of the data engineer varies from company to company. For example in some companies they will do etl or elt tasks ( e extract ) ( t transform ) ( l load) . some roles they will build pipelines . So overall as a data engineer u should be ready to perform various operations on data.

Data engineering roles can be identified based on their specific responsibilities within the data lifecycle. For example, a Data Engineer focuses on designing and constructing data architectures, while a Data Pipeline Engineer specializes in developing and optimizing data pipelines for efficient data flow. Additionally, roles like ETL Developer may concentrate on extracting, transforming, and loading data for analysis and storage.

Adopting containers and Kubernetes improves portability and robustness of data pipelines across environments. observability with metrics, logging and tracing is crucial for monitoring pipeline health and pinpointing failures. Multi-region architectures with data gravity minimize network costs and latency for global data pipelines.

Understanding cloud technologies like AWS and big data frameworks such as Spark is crucial for building scalable solutions. For instance, using AWS Glue and Spark, one can develop a scalable and reliable ETL pipeline. Another example involves using EC2, EMR, AWS Lambda, and Spark to construct a data pipeline, which offers greater flexibility in terms of efficiency and performance optimization.

A?adiría a este perfil conocimientos en DevOps o crearía un perfil nuevo llamado DataOps Engineer, que combina habilidades de operaciones para proyectos de datos. Un DataOps Engineer debe velar soluciones ágiles y automatizados, debe tener conocimientos sólidos en la nube y asegurar una buena gobernanza de datos.

Platform engineer not only should be well versed with data platforms rather at the same time should also be familiar with ML platform as well. Many times it happens that both Data Engineering teams and Data Science teams collaborate on the same infrastructure. Data platform engineer should also own and maintain data dictionary and data catalogue so that data can easily be discovered on the data warehouse.

The way these different roles are delineated seems to indicate a slightly siloed way of working, or maybe I was lucky to work E2E through all stages, starting from ingestion from multiple and varied sources up until ensuring that the numbers coming up on the reports are as expected. A Data engineer is expected to figure out the best strategy for consolidating data from various sources, orchestrate and optimize the pipelines created, ensure data quality across all layers and address data governance aspects. Infrastructure setup, report development and insight generation are responsibilities that are taken up by other personnel but I do have to concede that I find the title "data analyst engineer" weird, "data analyst seems more apt for me"

While working as a Data Engineer, I used LocalStack and Terraform to build a local infrastructure that mimicked the data pipeline built on the cloud. This approach greatly helped other data engineers, as they could quickly test their code locally and ensure it was working as expected before deploying it to the cloud. This practice saved a significant amount of time.

Leveraging cloud services provides autoscaling capabilities and reduces overhead of managing infrastructure. Schema management and governance ensures consistency as data assets grow and change. Modern data platforms focus on enabling self-service access and democratization of data.

Automated data testing using unit, integration and regression testing helps catch bugs early. Root cause analysis of bad data using techniques like 5 Whys helps address systemic gaps. Data quality metrics should tie back to business KPIs and goals to measure impact.

Blending statistical analysis with ML provides actionable insights from data. Visualization best practices improve consumability of analysis for diverse audiences. Ideate collaboratively with business and technical users to identify high-value analysis opportunities.

When it comes to analyzing data, the right kind of queries make a huge difference. During my time working, I used advanced SQL queries to get important financial and operational details from the data. It was more than just pulling out information; it was about digging deep into the data to find the key insights needed.

Having built teams from scratch and scaled here are my thoughts on identifying and building data engineering org. 1. Start with data platform engineers to build core infrastructure like data warehouses, lakes and pipelines. 2. Add data integration specialists to ingest and transform data from key sources. 3. Include data analysts early to derive insights and guide requirements. 4. Scale platform management with dedicated engineers as ecosystem enlarges. 5. Automate aggressively via pipelines, interfaces and monitoring to boost productivity. Fill specialized roles like data visualization, ethics and education based on needs and align hiring roadmap to data maturity curve of the organization.

Based on what I have seen in the industry for over a decade, in 2023, Data engineering roles can be broadly of the 3 following types: 1. Data Analytics Engineer - Focusing mostly on raw data available on the relevant platform and using it to create various types of reports & data products. They are close to end users. 2. Machine learning data engineer - Focusing on ML related pipelines and deploying ML products from 0 to 1. They work closely with Data Scientists. 3. ETL/ELT engineer - Responsible for ingestion and transformation of data from various sources into one common place. And all 3 of them are being done cloud these days with software engineering design patterns and help of Devops ( former DBAs) folks. #dataengineering #data