Here's how you can manage data privacy and security complexities in your career as a data engineer.

It is important to understand most of the legal frameworks and what they require from you to comply with them. One of the major ones is the General Data Protection Regulation (GDPR) if you're working with data in the European market. Similarly, there are other laws governing other regions or parts of the world as well. It ensures that you should be considerate while working with the personal or individual data and you should have the process in place to have them removed if they requested to do so. There could be a lot of big lawsuits against your organization and could lead to potential reputational damages as well if you don't comply with some of these regulations.

In the world of data engineering that’s always changing, Roles-Based Access Controls (RBAC) are a super important tool for making sure data stays safe and behaves well. RBAC is great because it helps control who can access what, making sure everything runs smoothly. It’s like a key player in managing access permissions, bringing extra security, and making sure everyone follows the rules. To effectively implement RBAC, careful planning, clear role definitions, and adherence to best practices are essential. As organizations grapple with the complexities of data management, leveraging RBAC becomes a strategic approach to enhancing data security and maintaining order within any group.

Data engineering isn't about only processing, and storing the data. It does require a lot of governance, and compliance with legal regulations. One of the fundamental ones is having proper access control in place so that users' information is only accessible to the right people. On the contrary, as an organization you want to democratize your data so that you as a business can offer more value to your customers. So, you can use the data but you need to have a proper access control mechanism in place so that the user's privacy is maintained and data is secure.

Data privacy and security are important while handling the PII (personally identifiable information) information of the users. So, using some of the anonymization techniques is one of the ways to achieve that goal. You can mask the data so that critical information isn't available to everyone. Furthermore, you can leverage the tokenization to replace the sensitive elements in your data such as credit card information, and DOB (Date of Birth). Also, when you need to share the data for reporting purposes with other domains, you need to ensure that either the data is aggregated, or it has been anonymized to maintain the use privacy.

There's a few key ways to consider for data security, but the key is to think of it as layers of protection. - Authentication with SSO and/or other mechanisms should be the first line of defense - RBAC roles provisioned with least-privileged principle should ensure access security - Dynamic Data Masking should be used to enable both obfuscating data for adhoc access while enabling privy consumers to access data - Data Tokenization should be considered if you absolutely want to ensure no data leakage and your app layer can convert to expose sensitive data; generally only do this on the most secure data like passwords/SSNs/Bank accounts - Data Anonymization is generally used for lower environments to fully obfuscate

A data audit evaluates who has access to the data and why. In contrast to auditing finances, data auditing looks at more than simply the total quantity to determine a data set's qualities. To ascertain the quality, importance, and utility of the data, you can perform a data audit by looking at the data sources, the data creation process, and the data structure. Furthermore, performing audits of data has turned into a crucial part of compliance efforts. Organizations subject to data protection laws, such as the CCPA and GDPR, are required to have audit logs on file. In the future, these rules are probably going to get stricter and more frequent.

Data pipelines will inevitably fail - no such thing as a bullet proof data pipeline. How you respond is everything. - Have monitoring and alerting processes in place to proactively escalate (consider PagerDuty only for the most critical failures that you do not mind waking up at 2am for) - Create a standardized process for Incident Response - Identify and setup an On-call schedule - Focus on solving the immediately impacted problem first - Communicate proactively to stakeholders (let them know you got it) - Perform a root-cause-analysis after the resolution (do this ASAP after immediate resolution) - Identify areas of improvements for long-term (ask the hard questions but NEVER blame)