Move data between systems, Etract transform and load

6 Responsibilities of a Data Engineer

Introduction

Are you a student, analyst, engineer, or someone new to the data space? If you find yourself unclear about the job responsibilities of a data engineer and believe that the current state of their job description is messy, then you’ve come to the right place. In this post, we will delve into the 6 key responsibilities that define the role of a data engineer.

Responsibilities of a Data Engineer

1. Move data between systems

Move data between systems, Etract transform and load
Move data between systems, Etract transform and load

The primary responsibility of a data engineer is to efficiently move data between different systems. This process typically involves three main steps: extraction, transformation, and loading.

  • Extraction: Data engineers extract data from various sources such as external APIs, cloud storage, databases, static files, or any other relevant data sources. This step involves gathering the required data from its original location.
  • Transformation: Once the data is extracted, it needs to be transformed to make it usable and relevant for further analysis. Data engineers perform tasks like mapping, filtering, enrichment, changing the data structure (e.g., denormalization), and aggregating the data. These transformations ensure that the data is in the desired format for analysis and storage.
  • Loading: After the necessary transformations have been applied, the data is loaded into the destination system. This system can be a cloud storage file system, a data warehouse, a cache database, or any other target system specified by the data engineering team. The loading step ensures that the data is securely and efficiently stored in the designated system for future use.

Common tools/frameworks used in this process include Pandas, Spark, Dask, Flink, Beam, Debezium, Kafka, Docker, and Kubernetes. These tools provide data engineers with the necessary functionality and infrastructure to perform efficient data movement tasks between different systems.

By effectively moving data between systems, data engineers enable seamless data integration, ensuring that data is available for analysis, reporting, and decision-making purposes across the organization.

2. Manage data warehouse

data warehouse
data warehouse

Data engineers have important responsibilities when it comes to data warehousing, where most of a company’s data is stored. These responsibilities include:

  • Warehouse data modeling: Data engineers are responsible for modeling the data within the data warehouse to optimize it for analytical queries. This involves applying appropriate partitions, handling fact and dimension tables, and other techniques to ensure efficient aggregation queries on large tables.
  • Warehouse performance: Data engineers work to ensure that queries run smoothly and efficiently within the data warehouse. They optimize performance, fine-tune queries, and ensure the scalability of the warehouse to handle increasing data volumes and user demands.
  • Data Quality: Data engineers play a crucial role in maintaining data quality within the data warehouse. They implement processes and tools to validate, clean, and monitor the integrity of the data to ensure its accuracy and reliability for analysis and reporting.

Common modeling techniques employed by data engineers in data warehousing include Kimball modeling, Data Vault, and Data Lake approaches. These techniques help structure the data in a way that supports efficient querying and analysis.

Common frameworks used for data quality in the context of data warehousing include Great Expectations and dbt (data build tool). These frameworks provide capabilities for data validation, documentation, and testing to ensure the quality of the data within the warehouse.

Data engineers typically work with popular data warehouses such as Snowflake, Redshift, BigQuery, ClickHouse, and Postgres. These platforms provide scalable and powerful infrastructure for storing and processing large volumes of data.

3. Data Pipeline Management for Data Engineers

Data Pipeline
Data Pipeline

Data engineers have the responsibility of managing data pipelines, which involves scheduling, executing, and monitoring the pipelines to ensure smooth data flow.

  1. Scheduling: Data engineers schedule the execution of data pipelines based on predefined schedules or triggered by specific events. This ensures that the pipelines run at the desired intervals or in response to relevant events, such as data updates or system triggers.
  2. Execution: Data engineers oversee the execution of data pipelines, ensuring that they can scale to handle large data volumes and have the necessary permissions to access the required resources. They manage the proper execution of tasks within the pipelines, orchestrating the flow of data from source to destination.
  3. Monitoring: Data engineers continuously monitor the data pipelines for any issues or anomalies. They proactively identify failures, deadlocks, or long-running tasks and take appropriate actions to resolve them promptly. They also manage metadata associated with pipeline runs, including timestamps, end-to-end execution times, and failure reasons, to facilitate troubleshooting and performance optimization.

Common frameworks and technologies used in data pipeline management include Airflow, dbt, Prefect, Dagster, AWS Glue, AWS Lambda, and streaming pipeline tools like Flink, Spark, and Beam. These frameworks provide the infrastructure and tools necessary to schedule, execute, and monitor data pipelines efficiently.

Data engineers work with various databases such as MySQL, Postgres, ElasticSearch, and data warehouses, ensuring seamless integration and data transfer between systems.

For storage, common systems utilized by data engineers include AWS S3 and GCP Cloud Storage, providing scalable and reliable storage solutions for data pipeline processes.

To monitor the performance and health of data pipelines, data engineers rely on monitoring systems like Datadog and New Relic. These tools offer insights into pipeline status, resource utilization, and potential bottlenecks, enabling proactive maintenance and optimization.

By effectively managing data pipelines, data engineers ensure the reliable and timely flow of data, enabling businesses to leverage accurate and up-to-date information for analysis, reporting, and decision-making.

4 Serving Data to End-Users

Serve Data to Analytics

Once you have data available in your data warehouse, the next step is to serve it to the end-users. Here are the common tasks involved in this process:

  1. Data visualization/Dashboard tool: Set up a tool that allows users to analyze data and create visually appealing charts and dashboards. Popular tools for this purpose include Looker, Tableau, Metabase, and Superset.
  2. Permissions for the data: Depending on the end-users and their access requirements, you need to grant appropriate permissions to ensure data security. This may involve setting up role-based permissions for your system, granting access to specific tables or datasets, or managing cloud user permissions if the data is stored in cloud storage.
  3. Data endpoints (API): Some applications or external clients may need programmatic access to your data through APIs. In such cases, you will need to set up a server to send data via API endpoints. This typically involves using programming languages like Python, Scala, Java, or Go to build and manage the API endpoints.
  4. Data dumps for clients: Certain clients may require periodic data dumps from your system. In such cases, you will need to establish a data pipeline to facilitate the transfer of data to their desired format. This ensures that clients can receive the data they need in a timely manner.

Common tools and languages used in serving data to end-users include Looker, Tableau, Metabase, Superset for data visualization, role-based permissions for system access control, and programming languages like Python, Scala, Java, or Go for API endpoints. Additionally, pipeline tools can be employed to facilitate client data dumps.

5. Data Strategy for the Company

Data engineers play a crucial role in developing the data strategy for a company. Here are the key aspects they are involved in:

  1. Data collection and storage: Data engineers help determine what data should be collected, how to collect it, and establish secure storage practices. They work with stakeholders to identify the most relevant data sources and ensure data is collected in a consistent and reliable manner.
  2. Evolving data architecture: Data engineers continuously evolve the company’s data architecture to meet custom data needs. They assess the requirements of different teams and departments, and design or modify data pipelines, databases, and storage systems to accommodate evolving data requirements.
  3. Educating end-users: Data engineers take on the responsibility of educating end-users on how to effectively utilize data. They provide guidance on accessing and interpreting data, as well as best practices for data analysis and reporting. This empowers end-users to make data-driven decisions and derive insights from the available data resources.
  4. Sharing data with external clients: Data engineers also play a role in determining what data, if any, should be shared with external clients. They collaborate with stakeholders to evaluate the risks and benefits of data sharing, establish data sharing policies, and implement mechanisms to securely share data with external parties when necessary.

Common tools and frameworks used in the data strategy process include collaboration tools such as Confluence and Google Docs for documentation, RFC (Request for Comments) documents for proposing and discussing ideas, brainstorming sessions, and regular meetings to align stakeholders and drive data strategy decisions.

By actively participating in data strategy development, data engineers ensure that the company has a robust and effective approach to managing data. Their contributions in data collection, architecture, user education, and data sharing help organizations leverage data assets for optimal decision-making and business growth.

 

6. Deploying ML Models to Production

Deploying ML Models to Production
Deploying ML Models to Production

When data scientists and analysts develop complex models that simulate specific business processes, it is the responsibility of data engineers to optimize and deploy these models in a production environment. Here are the key tasks involved in this process:

  1. Optimizing training and inference: Data engineers work on setting up pipelines for training and inference. This includes establishing batch or online learning pipelines that enable the model to continuously learn and improve over time. They also ensure that the model is appropriately sized, taking into account factors such as computational resources and model performance requirements.
  2. Setting up monitoring: Data engineers implement monitoring and logging systems to track the performance and behavior of the deployed ML model. This includes monitoring metrics, detecting anomalies, and collecting logs for troubleshooting and analysis. These monitoring systems help ensure that the model is functioning correctly and provide insights for further optimization if needed.

Common frameworks used in deploying ML models to production include Seldon Core and AWS MLOps. These frameworks provide tools and infrastructure to streamline the deployment process and manage the lifecycle of ML models effectively.

By optimizing the training and inference pipelines and setting up robust monitoring systems, data engineers play a crucial role in successfully deploying ML models to production. Their efforts ensure that the models can effectively support real-time business processes and deliver reliable results for decision-making and operational purposes.

Conclusion

This article has provided you with an overview of the various responsibilities that a data engineer may have. The specific responsibilities can vary based on factors like the company size, team structure, and workload. ‘

The primary goal of data engineering teams is to facilitate the effective utilization of data throughout the organization for informed decision-making.

In larger companies, your responsibilities may become more specialized and focused. You can use the responsibilities mentioned in this article as a guide to identify your areas of interest and ensure that your job responsibilities align with them. If you have any questions or comments, please feel free to leave them in the comment section below.


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