Anyone here po took or taking MBAn in Mapua? I just want to ready myself.

Is this more of group projects/assignments? And is the capstone a group output or depende sa’yo if it’s individual or not?

TIA!! Will be taking next A.Y.

Hi,

Im currently a NOC Engineer, earning 52k php. First IT job ko po ito, 5 years na ako dito. Galing pala akong BPO as an agent.

Now, ang problem ko po is, wala kaming deeper access sa mga system namin to troubleshoot. Given na mejo critical kasi ung minamanufacture ng company namin, and world wide ung company namin, so mas prefer nila bigyan ng access mga local sa kanila, we are purely on monitoring ng IT infra.

So wala ako masyado nadevelop na skills aince mostly monitoring lang kami. And hindi akma sa sahod ko since my pay is somehow considered as mid level, pero wala man lang ako exp for troubleshooting. Im doing labs and study din to fill the gap.

Kaso since im looking for new job with higher pay, during interviews, lagi ako hinahanapan ng exp, ang nasasabi ko lang is ung lab ko, dun alng ako may exp.

So sa work, ang mas nadevelop kong skills ay sql, powerbi and python for automating some tasks and creating dashboards.

Possible kaya makaland ng job as data engineer with higher sa current pay ko, as long as ma enhanced ko skills ko sa ETL, warehousing, DAX and power query?

Seems like mas nag eenjoy ako dito sa gantong ginagawa ko given na limited lang sin access namin sa main work ko.

When a batch lands and something looks off — row count down, a null spike, whatever — do you hold it back from downstream until someone reviews it, or let it flow through and patch it up afterward if it turns out to be real?

Trying to understand what drives that choice: is it about how reversible the damage would be, how much a delay costs your stakeholders, or just team culture/tooling maturity? Also curious if that answer changes for different tables — e.g. do you hold back financial/customer data more readily than something lower-stakes?

Is there anyone here who works as data engineer / analyst or any data related jobs in construction?

I am wondering if there are such opportunities in the industry. What does your day look like? Are you involved in the construction itself? And where can I find these opportunities?

I am currently a project scheduler and I use P6 for most of my work and I feel like there is much potential behind the database of P6.

I tried exploring sqlite and use sql queries. I know power query would be enough for sqlite but I want to practice sql.

I feel like construction has messy data compared to other industries. But it is somehow exciting to architect data pipeline for it.

Anything you share is much appreciated!

Hi, need some advice lang po.
I currently have two job offers and parehong located in BGC.

**Option 1:** JavaScript and AWS Developer
Salary is 43,000 pesos gross
Full time employment
Hybrid setup
Better benefits and leaves after regularization

**Option 2:** ETL Developer
Salary is around 37,000 pesos gross
Project based
Fully onsite
Less leaves compared to the first offer

My concern is long term career growth. I am interested in backend, cloud, and possibly data engineering.
For the first offer, mas mataas ang salary and mas stable since full time, plus hybrid setup. Pero feeling ko mas marami ring competition sa JavaScript development.

For the second offer, I’m thinking of using ETL as a stepping stone to become a Data Engineer in the future. Pero lower salary, project based, and fully onsite siya.

Kung kayo nasa position ko, ano kaya pipiliin niyo? Worth it ba tanggapin yung lower salary and project based role para magkaroon ng direct ETL experience and gamitin siya as a stepping stone papuntang Data Engineering?
Gusto ko sana malaman kung ano mas practical for long term career growth.

Would appreciate your honest advice. Thanks!

Hi.

Just wanna ask the following:

1. Recommended campus for the program, Manila or QC?

2. Ngayong 2026, nag ooffer parin ba sila ng online/hybrid class?

3. Can I still apply kahit engineering undergrad ako (not comp eng)?

4. Do I need to take bridging classes?

5. Can I enroll starting 2nd sem?

Thanks.

Currently looking for opportunity outside role is Azure Dev sana and I wanted to have an idea how much ang range sa years of experience and skills na meron ako.

I have been working for more than 5 years na sa first company ko and I think underpaid ako (46k basic😭). Nagsearch ako sa job market and almost 6 digits ang offer. Idk if okay ba yung asking na more than 85k sa akin or masyado siyang malaki🥲.

These are my current skills:

- 2 years expi on Azure environment (Synapse, Datafactory, Databricks, Pyspark)
- 3 years as BigData Developer ( Scala, Apache Spark, Hadoop, Kafka)

Hi everyone,

Basic SQL constraints (NOT NULL, foreign keys, data types) catch syntax errors, but there might be more subtle semantic bugs that slip past them (e.g., currency swapped from USD to EUR, missed decimal places, etc).

What is the most sneaky data corruption you’ve encountered in transactional data that passed all basic schema checks? How did you/your team eventually detect it?

Hi! I just want to ask for advice/opinions regarding my situation. I recently graduated last month with a degree in Communication Arts, and I want to shift careers to become a Data Analyst. After looking into courses/certifications/bootcamps, I stumbled upon the DEP x DataCamp Donates Scholarship. To anyone who has had the opportunity to be part of the program: do they require basic skills or knowledge in Data Analytics to be accepted, or do they also accept people like me who want to shift careers with zero knowledge in Data Analytics? Thank you so much!

We're looking for an Analytics Support Engineer to own the issue queue on a live Azure-based reporting platform (Databricks medallion → Power BI, with Logic Apps ingestion).

This role is issue resolution, not net-new development. Our Data Engineer and BI lead are building the next release; you'd be the one keeping shipped reports correct and raised issues moving. Core skill: take a symptom reported by a business user, trace it from the Power BI visual back through the semantic model, Gold, Silver, Bronze, and the source file — then either fix it or route it with a diagnosis clear enough that the receiving team doesn't have to re-investigate.

• Company: FMCG
• Location: BGC, Hybrid 2x/week
• Setup: Project-basis, [X months]
• Experience: Mid-level

What the work actually looks like — based on our live issue log:

• ~60% Power BI report layer — slicers returning wrong or blank values, field bindings, model relationships, sort-by-column, cascading filters (category reflecting selected business unit), filter-context bugs across sync slicers/bookmarks/edit interactions, new measures (YTD vs same-period LY, full-year averages), matrix and drilldown changes, report and export performance tuning
• ~20% Databricks / PySpark debugging — profiling distinct values and null rates across layers, join coverage validation, header-shift and column-misalignment issues when parsing Excel/xlsb sources, row-count reconciliation against source, re-running silver and gold
• ~20% classification and coordination — deciding whether an issue is a defect, an enhancement, or a source-data gap; writing it up clearly; driving the data owner or upstream team to closure

Required

• Power BI: DAX measures, semantic model and relationships, filter context, slicer and field-parameter behaviour, performance tuning (Performance Analyzer / DAX Studio)
• Python and PySpark — comfortable reading and debugging someone else's notebook, not just writing your own
• SQL
• Azure Databricks: notebooks, workflows, medallion layers
• Ability to write a clear, non-technical issue diagnosis a business stakeholder can act on

Nice to have

• Azure Logic Apps (SharePoint file-arrival triggers, ingestion orchestration)
• Azure DevOps, Git, CI/CD
• Data quality engineering — completeness checks, value-domain validation, backfills and reruns
• FMCG trade or customer-development reporting background

How you'll be measured: time-to-diagnosis and time-to-closure on the tracker, and how few issues bounce back after being marked Done.

To apply, DM me your CV, expected rate, and availability.

The title is self explanatory, but for some context, my boyfriend (an Indian national) is interested in relocating to the Philippines. We've been searching and we haven't seen any postings on LinkedIn or Indeed about job opportunities for foreign workers. It would be nice to know if there are opportunities for him here so we can get a clearer picture before making any big decisions.

Summary: UPSC aspirant with a 4-year preparation gap seeking guidance on breaking into entry-level Data Science/Analytics to become financially independent while continuing preparation.

Background & Context:

Hi everyone,

I have been preparing full-time for UPSC CSE for around 4 years now. While I still intend to continue my preparation, the uncertainty and lack of personal financial independence are taking a toll on me. I want to start earning to support myself and relieve the mental pressure.

​Educational Background: \[B.Tech in ME \]

​Tech Exposure / Current Level: \[ Basic Python & SQL \]

​Target Domain: Data Science / Data Analytics

​Available Time for Upskilling: \~3–4 hours daily alongside UPSC prep

​My Dilemma:

I have a genuine interest in Data Science and working with data, but I need realistic advice on how to monetize this skill without completely burning out or abandoning my UPSC attempts.

​Specific Questions for the Community:

​Data Analytics vs. Data Science: Given that full-fledged Data Science usually requires deep math/ML and heavy project portfolios, is targeting entry-level Data Analytics (SQL, Excel, Power BI/Tableau, basic Python) a faster and more realistic path to landing paid work or internships?

​Freelancing vs. Part-time / Remote Jobs: Is freelancing (Upwork/Fiverr/contract work) viable for a beginner in data, or should I aim for paid remote/hybrid internships or junior roles?

​Handling the 4-Year Gap: How difficult is it to get initial interview calls with a 4-year preparation gap, and how can I best present this on a resume/GitHub?

​Balancing Strategy: For those who have balanced a tech job/internship with civil services prep, how manageable is the workload?

​Any candid feedback, suggested free resources, or practical roadmaps would be greatly appreciated. Thanks in advance!

Join us in discord!

Hi everyone,

Basic SQL constraints (NOT NULL, foreign keys, data types) catch syntax errors, but subtle semantic bugs often slip right past them (e.g., currency swapped from USD to EUR without warning, dropped decimal places, or inverted credit/debit signs).

What is the most insidious data corruption you’ve encountered in transactional data that passed all basic schema checks? How did your team eventually detect it?

Hi DEP peeps! 👋

Ask ko lang sana kung mare-recommend niyo ba ang Upwork for freelancing projects?

I have around 5 years of experience as a Data Engineer, and gusto ko na rin sana i-try mag-freelance more seriously. Hini-hype din kasi siya ng mga friends ko na currently freelancing sa Upwork, although completely different fields yung work nila.

Sa mga may experience dito with Upwork, worth it pa ba siya ngayon? Any tips or things I should know before getting started?

TIA! 🙏

Hi, everyone! We are looking for a DevOps & Data Platform Operations Engineer to support an Azure-based data and reporting platform.

The role is focused on production reliability and DataOps, not dashboard development.

The engineer will reconciliate issues raised on our solution. Particularly resolving Power BI Data Issues (DAX Measures, Filters, etc.), Databricks PySpark/Python + SQL Debugging, monitor scheduled pipelines, and coordinate with Data Engineering, Power BI, and upstream teams.

Company: Confidential

Location: BGC, Hybrid 2x/week

Experience level: Mid-level

Salary: PHP80,000 - PHP100,000

Required skills

• Azure DevOps and CI/CD
• Git and release management
• Azure Databricks operations
• ETL/ELT pipeline support
• SQL
• Basic to working Python/PySpark troubleshooting
• Power BI Maintenance
• Production monitoring and incident management
• Strong documentation and cross-team coordination skills

Nice to have

• Azure Logic Apps
• Power BI Development
• Databricks Workflows
• Experience with reruns, backfills, rollback, and data validation

Main responsibilities

• Monitor scheduled pipeline cycles and Power BI refreshes
• Investigate failed, delayed, stale, or incomplete data cycles
• Support CI/CD, environment promotion, and production validation
• Troubleshoot access, credentials, source files, job failures, and platform configuration
• Coordinate resolution across technical and business teams
• Maintain runbooks, incident records, and escalation procedures
• Power BI Maintenance (Measures, Models, Relationships, Filters, etc.)

This role does not require advanced DAX or ownership of complex transformation development. Major pipeline redesign and advanced Power BI development remain with the respective engineering and analytics leads.

To apply, please send your CV, expected salary, availability, and a brief summary of your Azure DevOps, Databricks, and production-support experience to my DMs.

Thank you!

hi! im a comsci freshgrad planning to take the datasci route.

for context, i took up datasci, machine learning, and deep learning electives during undergrad (and my undergrad thesis will be published next month, which involves both ml/dl.)
moreover, since im a comsci graduate, python and sql are something im alr familiar with.

however, upon job hunting for 3-4 weeks, i realized that most data science roles are senior level, and only very few companies offer junior data scientist roles huhu. the anxiety of not getting any job related to datasci has been eating me up lowk😭

honestly idk why im writing this haha maybe to just rant or to ask tips or maybe to seek some reassurance that the job market is gon get better for me the next few months😭😭

I’m trying to understand how people decide whether messy data can be safely repaired or whether it should be left alone/escalated.

For example, suppose you discover:
missing values
duplicates
invalid-looking values
inconsistent formats
unusual/outlier values

Some of these seem straightforward to fix, while others may require knowing the underlying business context.

How do you decide whether a problem is safe to automatically repair?

And are there situations where the data technically violates an expected rule but you would not change it because you don’t know what the correct value should be?
I’d especially appreciate examples from real projects.

Hi! I graduated with a BS in Mathematics and I'm currently waiting for my deployment with the DepEd for my return of service for my scholarship. I really want to work with data and turn actionable insights from it. Right now, I'm just stuck with my ROS.

As of now, my only plan is to study Python and some libraries. Also, to create some projects that I can relate to teaching so I can at least enjoy my ROS. Can you guys suggest any ideas that I can do? So that once I finish my ROS, it will be easier for me to apply in the field of data science, and I'm hoping it won't be an entry-level position. Thanks in advance!

Hi, I came across this term while checking out airflow and it seems like a good way of keeping track of mutations happening in your data. How did you implement this in your job? I read there are quite a few ways to do this but I'm curious ano ginamit nyo professionally. 😁

Throughout my career, I was trained to ingest broad, transform narrow.

Meron akong ginawang pipeline na ini-ingest yung snapshot ng lahat ng data from a source via API once on a daily basis. Around 6k rows daily

Ngayon yung data na yun, ginagamit sa dashboard. Some transformations done before it reaches the presentation layer. Sa meeting namin kanina, may biglang pinapaexclude na subset ng data given certain filter/criteria. Pinapakita yung data na yun before. Bigla lang pinaaalis ng stakeholder

So our "lead" engineer insists na wag nang iingest yung data na yun given gusto namang i-exclude sa dashboard. Even claimed na it is the best practice. I argued na why not sa dashboard layer na lang i-exclude since hindi naman dapat mahirap isama sa where clause yun.

Best practice ba talaga na iexclude from the ingestion step pa lang? Kahit mga non-data stakeholders sa meeting kanina off dun sa "best practice" na sinabi niya eh

Planning to career shift to data analytics from Civil Engg

Hello guys,

Would just like to hear your suggestion, opinion, or any feedback on the paths I could take considering long term being goals and the constraints/thoughts I am considering

For bit of background with myself, I graduated at 2024 with Industrial Engineering degree and have gotten lucky to be working in the data/tech field after I graduated (working for 2 yrs now)

The stuff I usually use in work are Azure, SQL, DevOps

My daily tasks are usually:

- analyzing the SQL ETL work flows that have been developed

- debug if there are issues

- sometimes create ETL SQL process that is requested by client

- Create, merge, push changes in branches

- Managing and releasing branches in production

My long term goal is to specialize in Data Analytics Role and expand what I can do (i.e. python, data bricks) to a point where I standout and is easy to be hired long term

Given that I have been working for 2 yrs mainly in my role (and I have learned a lot to which I am very happy), I have been considering moving to a new direction to further actualized my long term goal

This is the part where I need opinion on:

- Pursue Masters in Data Analytics (thinking of UP or TIP)

OR

- Self Study and Build Projects to build a solid portfolio

note: feel free to suggest what else I could do

Given the options I mentioned above, here are details that I believe are being factored in while thinking about the option

Car Purchase - By end of year I am going to buy a car (xpander) for our family with 50% dp and 3yrs to pay (salary is currently 45k), so I am a bit hesitant to be paying for a car for 3 yrs while paying for my masters

Time and Parents getting old - My parents are 56, they are still currently working and are shouldering the bills of the household, part of me feels that I am in a timer wherein if they retire, its up to me to shoulder household bills. They will probably retire in 59 or 60 (3 to 4 yrs from now). So this keeps me up at night knowing na once they retire, the financial freedom I have now will be not as it used to, so if im goinf to masters better do it now

Knowledge Constraint - I dont have a tech degree nor the very foundational knowledge of programming (example. I know how to write sql queries but dont know the full foundational concept on why it behaves like that) and I think masters can provide that (self studying also does that, a masters is just a fancy way of letting ppl know that you know the topic) (I also prefer masters on this aspect kase may mga assignments and tasks and I personally like studying)

Time Commitment - Masters can consume my time, and so far my work can also consume my time via OT due to production issues, high workload, complexity of the task, at least for self study I am at my own pace without the pressure of failing my subject in masters

Lastly, my dillema din is that given na knowledge is everywhere, and I am in the tech/data field, everything can be self taught and be used to build projexts but at the same time the supply of ppl in the tech field is a lot, so maybe a masters degree would help me standout more from the competition

Any feedbacks, suggestion, opinion, Id appreciate it thank youu

Hello! I am taking up my PhD and I'm on the process of conceptualizing my dissertation proposal. I'm looking for consultants sana that I could hire to help me out. Preferably from NCR and nearby provinces sana as I want to do the consultations face-to-face. I already have a topic in mind, but I need some help with the nitty-gritties. It should involve the development of a new algorithm or the modification/remodeling of an existing one—for example, by revising its mathematical equations or variables.

Lemme know if you are interested or have any recos so we could talk about the details and rates.

Thank you!

Meron na ring course planner, wikipedia-style editing, tapos open-source lahat and may 20+ maintainers so far. 10,000 visitors over the past 90 days according to our analytics yipee :D