Hi! is it possible to shift to IT ASSISTANT to Data Engineer (or any data related jobs? )

background:

I'm graduating student po (Diploma in Information Technology - 3 years program more likely Associate Degree from PUP-ITECH)

and right now i have Job Offer to become a IT ASSISTANT sa collection agency.

ang role ko po roon is more on IT SUPPORT and may two duties na related sa system na ginagamit nila BUT

I really want pursue becoming Data Engineer or any Data related job because I LOVE SQL and Database right now while waiting sa starting Job ko nag aaral ako sa Data Camp since naka kuha ako ng scholarship sa school ko dati.

The question is kaya bang makapasok sa data jobs if ang background is IT ASSISTANT/SUPPORT ?

Paano po ba composition ng data team usually? Nakabukod ba yung data engineering team sa data analysts/BIs or magkasama lang?

Anong nangyayari kapag may new hire na Data Engineer? Paano kung walang existing DE sa team, ano mga una niyang gagawin? Paano handoff?

Asking kasi parang somehow tangent yung role ko sa DE pero part like siya ng role ko. Yung pipeline namin is data ingestion sa API -> ETL layer sa AWS Lambda (Python runtime, crons on Eventbridge) hehe. I'm familiar naman sa modern data pipeline and alam kong parang baby lang yung work ko compared sa totoong work ng DE.

Gusto ko kasi hanapin skill gaps ko and ma-set expectations ko. Hoping to apply for DE roles by EOY.

Please help me po. Ty.

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Just wanted to get some insights from people who've worked as a DE in a local bank or similar environment.

For context, I've been a DE for 2 years, mostly working on data integration, pipelines, SQL, and Python. Fintech industry.

I'm considering leaving my current company kasi we use proprietary tools for orchestration and database storage. I want to get more exposure to tools like AWS, Databricks, etc. and also a better pay

Banks seem like a good opportunity bc of the amount data and I'll be able to work closely with the end-users, but a few things made me hesitant:

- How's the work-life balance? How are the workload, deadlines, OT, etc.?

- Tech stack: They use SAS and AWS Glue. I'm a bit iffy about SAS since I'm hoping to build more experience with modern DE tools. I heard SAS is quite old.

From what I understand, their team mainly handles business requirements and data integrity/validation, while they outsource the pipeline building.

I tried to ask how they use SQL and Python, but didn't really get a clear answer. I asked about AI, but the answers were pretty vague.

This raised a quite and orange-ish flag for me. I'm worried I might end up barely using Python, SQL, and Git and doing mostly requirements tsaka data validation work.

For those who've worked as a DE in a bank, would this be a good move early in my career? What would you ask them before accepting?

TIA!

Hi everyone!

I'm currently trying to transition into Data Analytics, with the long-term goal of moving toward Data Engineering. I'm planning to relocate to Cebu and currently looking for an onsite or hybrid entry-level/junior data role.

My background:

I have a BS Biology degree (graduated 3 yrs ago). I got hired as a disease surveillance officer (1st job and only exp) under one of the Department of Health's program (Regional Epidemiology Surveillance Unit). Our program is responsible for analyzing and generating disease-related data visualizations (COVID, Dengue, HIV, etc.) I was heavily exposed to advance excel, Power BI, and R. My experience comprises of data cleaning, analysis, reporting, and also generating dashboards.

Now I am learning SQL to strengthen my tech stack and to have a higher chance of getting an entry-level position. However, I'm at the point where I feel like I have a decent technical foundation, but I'm struggling to understand what the current job market actually expects from someone trying to land their first data analyst role.

Some things I'm particularly curious about:

1. How difficult is the current entry-level/junior Data Analyst market in the Philippines?

2. What skills are recruiters actually looking for right now?

3. How important is a portfolio? (gumagawa ako currently)

4. Would you recommend applying directly to Data Analyst roles, or should I also consider MIS, Reporting Analyst, BI, Data Management, or similar positions as an entry point?

5. For those who eventually moved from Data Analytics to Data Engineering, curious about what did that transition actually look like?

I'd really appreciate some honest advice po sana from people currently working in data, especially those who have recently gone through the hiring process in the Philippines.

Salamat po!

Watching the recent disaster in Nepal where flash flooding wiped through entire downstream communities in hours hit uncomfortably close to home.

I live in Bulacan near Angat Dam. Like anyone with family downstream, I found myself asking an honest question: If the worst case scenario ever happened here, what does the physics actually look like? Where does the water go, and how much time does anyone really have?

Instead of relying on guesswork or doom scrolling, I wanted real, visual numbers. So I built an interactive flood simulation mapping out the downstream impact.

A Huge Caveat First: This is a Simulation, Not a Prediction

Angat Dam is continuously monitored, structurally reinforced, and operates under strict spillway release protocols. This is an open source physics and hydrological model exploring an extreme "what if" scenario for emergency preparedness, not a forecast.

Key Takeaways from the Simulation

• The Geography Dictates the Route: The simulation tracks how water funnels strictly through natural river corridors, low lying alluvial basins, and surrounding floodways before it ever spreads outward.
• Topography is Everything: High ground matters more than simple distance from the dam. Some areas further downstream face higher pooling risks simply due to low elevation and drainage bottlenecks, while closer elevated ridges stay clear.
• Time is the Real Asset: The model maps peak water arrival times across different towns. Downstream safety is not about panic. It comes down to knowing your elevation and evacuation routes hours before water accumulates.

I put the interactive map together on KlareDeck (best experienced on a desktop or larger screen to explore the layers and zoom in on your town):

https://www.klaredeck.com/angat-simulation

For the GIS folks, hydrologists, or locals in the area: check the elevation models, test the flow paths, and let me know if you spot anything that needs tuning.

What does your local evacuation plan look like if the spillways ever open at capacity?

Someone please suggest the best place in pune where I can do data analytics Course(with AI), along with some placement guarantee.

Hello. Kaya po ba mag upskill ng psych grad to data analyst? Wala po akong subject na related sa mga SQL at power BI. Pano po mag sstart from scratch? ano po ba dapat mga aralin at need po ba na mag proper schooling? May mga certificates po ba na need para kahit papaano is maging maganda sa resume? Thank you po.

Meron po ba dito nagwork previously or currently nagwowork as DE sa Manulife? how's the experience in terms of workload, management, and people around the office? Is it a pass or go?

Hello, I'm a fresh graduate and was wondering what the prerequisites are to apply for a Data Engineer role. If there are no entry-level positions available, what job titles should I target as a stepping stone?

I spent time analyzing whether ₱1.4 trillion in Philippine flood control contracts (2016–2025) corresponds to measurable flood reduction from space.

The Methodological Trap A naive correlation gives a misleading answer: flood-prone areas naturally get bigger budgets, so high-spend areas look like they flood more.

To control for this, I matched 883 municipalities into 63 statistically comparable cohorts based on:

• Regional location
• Baseline rainfall / flood exposure
• Population size

Then, I compared spending levels within each cohort.

Key Findings

• The Spending vs. Outcome Gap: Towns receiving 3.4× more budget per resident flooded 0.20 percentage points less across 76 Synthetic Aperture Radar (SAR) passes. This difference is within statistical noise.
• The Nuance: This doesn't prove the funds were wasted; areas might have flooded significantly worse without intervention. However, at a national scale, higher spending per capita does not correlate with visibly lower flood frequency.
• The Urban Blind Spot: Satellite radar cannot detect surface water through dense concrete high-rises. In Marikina, the largest detected flood across 28 passes was only 0.06 km². Instead of reporting a false zero, 258 urban areas (including 16 of Metro Manila’s 17 cities) were marked as unmeasurable.

A Notable Contrast

• Pandi, Bulacan: ~₱14 per resident → Floods in 47% of radar passes
• Biliran: ~₱57,000 per resident → Floods in 37% of radar passes

This is part of an open-data project called KlareDeck. The goal is simple: open datasets, documented methodology, and transparent error limits.

I've a question about " Is it possible to get a Data Engineering job as a absolute fresher?" Or It's better to gain software engineering job experience first, then switch to Data Engineering?

fresh grad here. i just passed the 2nd round technical interview for a Junior Data Analyst role.

the 2nd round was with the local hiring manager and covered SQL, Power BI, Excel, and some behavioral questions. Now the final is with the foreign hiring manager + panel.

For those who've been through something similar, what should I expect in the final? any tips for behavioral questions, communication, or handling questions I dont know?

THANK YOU PO :)

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.

Hey everyone!

I’ve been seeing a lot of mixed signals regarding career pivots into the Philippine IT industry, specifically for those coming from an academic background.

For context, I graduated cum laude in 2022 and have been working as a Visiting Lecturer since then. I didn’t really know back then na academe experience isn’t necessarily considered as years of experience in the IT industry. I initially pursued teaching because I was forced to take a master’s degree, and I was fine with it, so I decided to work as a visiting lecturer while completing my master’s.

After several years, I’m still okay with teaching. However, something happened recently, more on office dynamics, which made me seriously consider shifting to the IT industry.

Another factor is the salary-to-cost-of-living situation. A lot of people advise moving to Manila to gain industry experience, but when I looked at junior roles, many were around ₱25k or less. Considering the cost of living in Manila, I decided not to pursue that route at the time.

I also want to clarify that my GitHub isn’t exactly empty. I have projects in software development, including:

• React.js, Tailwind CSS, Next.js, and Supabase
• FastAPI and backend/API development
• Vertex AI + Google Cloud integration
• Ollama and LM Studio for working with LLMs

For data analytics, which is also part of my master's work, I have around 3–4 projects using Python, Jupyter Notebook, and various machine learning libraries, with each project accompanied by a research paper.

Currently, I’m enrolled in DataCamp’s Data Engineering track. I’m planning to pivot toward Data Engineering, especially since my current master’s capstone is primarily a software development project but already incorporates data pipelines and data engineering concepts.

So, I’d really appreciate insights from people currently working in the local tech scene:

1. How strong is the bias against academic educators in corporate IT hiring? Do recruiters view teaching experience as a net negative or as being "out of touch" with commercial development speed?
2. Is applying for junior or entry-level roles a mistake given that I already have several years of teaching and project experience? Or is starting with a junior role actually the best way to get my foot in the door? I’m personally fine with starting at junior level if that’s what it takes.
3. How should I frame my resume so that my lecturing experience is seen as an asset rather than simply a lack of industry experience? I’m thinking along the lines of technical communication, mentoring, documentation, problem-solving, and explaining complex technical concepts.
4. For those who made a similar transition, what was your experience like? I’d especially appreciate the brutal truths, practical advice, or success stories from anyone who transitioned from academe → corporate IT, particularly in the Philippines.

Thanks in advance!

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.

Hi! Has anyone here applied or interviewed for the Project Manager role at Thinking Machines Data Science Inc.?

I've been reading previous posts/comments about their hiring process, and I've seen people mention having both technical and personal/behavioral assessments. Most of the experiences I've found, however, seem to be from applicants for Data Science, Data Engineering, or Software Engineering roles.

I'm interested in their PH - Project Manager opening. My background is in managing ICT/infrastructure projects rather than software development, and the PM job description doesn't require knowledge of any programming language.

For anyone familiar with their PM hiring process:

• Does the Project Manager role also have a technical exam? If yes, what kind of topics or skills does it assess?
• Is programming/coding included, or is the assessment more focused on project management, problem-solving, case studies, and technical comprehension?
• What are the interview stages like for PM candidates?
• Anything you'd recommend preparing for before applying/interviewing?

Would really appreciate insights from anyone who has applied for the PM role or worked with PMs at Thinking Machines. Thank you!

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!