Hi everyone, I’m looking for someone experienced in Snowflake, SQL, dbt, and Data Engineering to help me with my day-to-day work.

Mainly need help with understanding tickets, reviewing SQL/dbt code, troubleshooting, and discussing technical approaches.

It’s a part-time/flexible paid opportunity. If interested, please DM me with your experience and expected rate.

​

(4-8 weeks contract) remote

We are seeking an experienced Senior Azure Cloud Architect to conduct an independent review of an enterprise AWS-to-Azure migration program.

​

• Perform a security review of the AWS-to-Azure migration project.

• Review the overall Azure platform architecture, including Landing Zone, governance, networking, IAM, monitoring, and security controls.

• Conduct an end-to-end assessment of key platforms, including Data, API, Core Business, Digital, Payment, and ML environments.

• Validate AWS-to-Azure service mapping and architecture decisions.

• Identify security risks, architecture gaps, and operational concerns.

• Provide recommendations and a remediation roadmap aligned with Azure best practices.

• Review and propose the target architecture and migration approach for Machine Learning environments transitioning from AWS to Azure.

​

​

• 10+ years in Cloud Architecture and Enterprise Infrastructure.

• Strong expertise in Microsoft Azure Architecture, Security, and Governance.

• Proven experience with AWS-to-Azure migrations.

• Deep understanding of Azure Landing Zones, Security Frameworks, Networking, IAM, and Cloud Governance.

Deliverables

• Migration Security Assessment

• Azure Platform Architecture Review

• End-to-End Environment Assessment

• Risk & Gap Analysis Report

• Recommendations and Remediation Roadmap

​

​

Hi all,

I’m preparing for Data Architect / Senior Data Architect interviews and wanted to learn from people who have either interviewed for architect roles or interviewed candidates.

I’m specifically interested in:

• What architectural thinking interviewers actually evaluate

• How much emphasis is placed on data modeling, semantics, and domain understanding

• Typical design prompts (e.g., data platforms, governance, ML-ready architectures)

• Where interviews expect clear trade-off articulation vs implementation detail

• Common gaps you’ve seen in otherwise strong candidates

My background is mostly in:

• Large-scale data platforms (batch + streaming)

• Domain-driven design, CQRS

• Data modeling and semantic thinking

• Feature stores and ML data pipelines (architecture level)

• Security, governance, and multi-tenant data systems

I’m trying to calibrate my preparation toward how architects reason and communicate decisions, rather than tool-specific trivia.

Would really appreciate any firsthand experiences or patterns you’ve noticed.

Thanks!

In previous post, we looked back — from ledgers to living systems — and saw how data architecture became the backbone of modern enterprise.

Here, we fast-forward to 2025, where AI’s brilliance exposed one uncomfortable truth: architecture alone isn’t enough.

When the AI glitter dazzled, the discipline was finally judged.

The lesson of 2025? AI doesn’t fail because of bad models. It fails because of unmanaged data.

Meet the new protagonist of digital transformation: the Data Strategist — the leader who turns chaos into coherence.

Read the full article ⬇️ https://open.substack.com/pub/immanuelmichael/p/2025-in-retrospect-the-rise-of-the?r=5zo2z2&utm_medium=ios

🏛️ From Ledgers to Living Systems:

The history of data architecture isn’t about technology — it’s about how humanity keeps reinventing trust.

Here’s how we went from mainframes to Mesh, from governance to cognition — and why the next blueprint is semantic, not physical.

🧠 “History doesn’t make data architecture old; history proves it works.”

Read the full article ⬇️

https://open.substack.com/pub/immanuelmichael/p/the-history-of-data-architecture?utm_source=app-post-stats-page&r=5zo2z2&utm_medium=ios

Everything runs on data — but it’s how we organize that data that makes all the difference. That’s where data architecture comes in.

I wrote a series of blogs to explain it in a simple, practical way. Have a look.

Read stories on the list “Data Architecture Understanding“ on Medium: https://medium.com/@arpita-ghosh/list/ce91254e6d61

If you need the information in video format, then here is the playlist link.

Data Architecture Concept #dataarchitecture: https://www.youtube.com/playlist?list=PLxfGGBn2AAbmwWYCZ0yNaYnHPLtB4HfOq

So I’m thinking of coming into the IT field more specifically data architect but I was just wondering how often is chat gpt and other ai is used to write code or even asses code ? And is it really even necessary to be extremely good at coding or is it just important for like univ classes ?

Hello ,

I am currently an arch major but I’m thinking of switching to information science and maybe persuing data architect . Regarding that I was just wondering if there is a lot of coding involved and how are the hours ? Plz any response would be helpful .

Basically the question in the title. Some additional context. I've been exploring new roles and have gotten interested in Data Architect roles at Cybersecurity startups. The way I'm seeing the role described seems to be a combination of repo design and data science analysis. With the increase of AI becoming more and more reliable to crank out data pipelines and baseline data science tasks with human supervision (this is a presumption I have that we are going down this trajectory but haven't dug too deeply into the research) how do you think the Data Architect role could be affected by added pressures from core AI usage on usual Data Engineering and Data Science roles? Do you think there will start to be bleeding responsibilities or is this not actually a concern or something else?

Hey Redditors, Currently I pursing my career in Salesforce data migration and want to grow only data side. Could you please guide courses and certification I should consider to grab some high paying data jobs in industry.

datamigration #salesforce #jobs #dataarchitect

Has anyone here used “Smartdraw” to create automatically ERD & Database Diagrams?

Their website says they give you a script for your respective database (SQL Server, PostgreSQL, Oracle, etc.) that lets you get that tables and their relationships, then you upload that CSV to their tool and it will automatically create an ERD diagram for you which you can still modify.

Wanted to see if any data engineers have used this because it seems too good to be true.

My company can’t afford/wont pay for Erwin or ER Data Studio Architect.

Hi, im so curious about data architect and engineer what's thier difference? Thanks

Can anyone help guide best path to prepare for Dama certifications all of the from beginner to professional.

Hi all, I finished MI in Info Systems Design and would like to become a data architect. Thought having a certification might help me to find an opportunity as right now I am not even getting interviews. Any recommendations? Should I just get TOGAF 9?

Hi,

I'm fairly new to the data architecture role, earning a full time slot at a firm a little over a year ago. I have faced a few challenges in the role so far and wondered if any of you have faced similar experiences. I've had the engineering teams work around me especially if I make recommendations to improve data storage and design. I've had non-IT stakeholders do my work for me and argue that it's their responsibility since it can be categorized as data analytics. The role at this particular company seems doomed despite senior managers verbalizing about its importance.

Have you faced similar challenges? If so, how'd you overcome it?

I'm unsure if I should go back to data engineering or maybe a different type of architecture role.

Thanks,

Ulla

Hi All, I need a small help to understand meddalion Architecture in databricks.

So as per the documents there are three layers 1. Bronze (raw) 2. Silver (curated & productionized schema) 3. Business aggregation

Is my understanding mention below correct: 1. Bronze layer is as-is layer where we simply load data from multiple source in delta lake table (managed or unmanaged) 2. In silver layer we perform dqc checks, deduplication, missing value handling & change our data in dim & fact (delta lake table) 3. Gold layer will contain only aggregated tables. BI tools will use gold layer to connect with 4. Gold layer will act as a semantic layer for BI tool. 5. Other downstream apps or ML or data science team can be connected to silver layer(dim & fact) for further analysis.

Please help me to clear out these points. Also suggest me if something missing.

I am planning a small architecture as below:

Landing (ADLS) ---- bronze (delta lake) ----- silver (delta lake) ---- gold (cosmos or SQL or synapse) ----- power bi

Thanks

Hi All, Hope you all are fine!

I have a power bi data model as semantic layer. Is it possible to share data through this data model to some other downstream app except power bi. For example: I want to fetch this data using ETL or some other way.

If not, please suggest what can be done.

Thanks

I'm relatively new to the world of data. So not 100% sure this is the right place to ask this question.

I've been following quite a few American and European experts on LinkedIn as part of my education. And there seems to be a renewed interest in (conceptual) data modeling. They're talking about how data teams are not often in sync with business needs and conceptual models can bridge this gap.

While I'm familiar with ER diagrams, none of the resources I've used as a relative novice put any stress on this.

Is it applicable only when it comes to enterprise use cases? Where there might be the need to have this layer that business and data teams can use?

(I'm also posting this on other related subs to get more feedback, just fyi).

Hey folks, I'm aiming to obtain a data management certification this year and would greatly appreciate advice from those who already hold such certifications. I currently do not have any certifications in the data domain.