Dare to Data

I founded Dare to Data to formalize and structure the relationships I have with some of the world's leading companies to consult, support and empower them in their data and AI journey.

Current and past clients include PepsiCo, PUMA, Whirlpool, Nitto, Amazon Web Services, AtScale, EM360, O'Reilly Media, nexxworks, Aporia, Colquímica, DATAcated Plus, The Ravit Show, Ontotext, Growth Tribe, IBM / Databand, CoRise, SingleStore, Equals, Einblick, Impetus, Nexla, Starburst, ClearML, Nvidia, Dubai Future Foundation, ThriveDX, Global DIY-Summit, Afera, Prophecy, Listly, Dremio, amongst others.

Founder

LinkedIn

Over 65,000 learners to date! Building and teaching content as a part of the data course library, currently focused on empowering others to take full control of their data through the use of SQL as well as how to tap into the power of analytical thinking.

LinkedIn Learning Instructor

IE Business School

Delivering keynotes for the US, Spain, Canada, Portugal, Russia, Armenia, Georgia, Turkey, Azerbaijan, Jordan, Belgium, Luxembourg, Norway, Switzerland, Chile, Greece, Japan, South Korea, Nigeria, Egypt, Lebanon, China, Indonesia, Saudi Arabia, France, Cyprus & more.

International Speaker

Graduate-level courses taught primarily within the MBA programs covering data and artificial intelligence topics, as well as personal branding workshops.

Adjunct Professor, Department of Information Systems and Technology

Leading the Corporate Masters program for Saudi Aramco employees.

Academic Director, Saudi Aramco Master in Business Analytics and Big Data

Leading the online Executive program which explores the range of possibilities offered by using new technologies and aligning digital strategy with general strategy in a company.

Academic Director, Digital Strategy and New Technologies Executive Program

Porto Business School

Graduate and executive level courses taught in business strategy for data and artificial intelligence.

Adjunct Professor

ISDI

Launched, continue to coordinate and teach the Data & AI module within one of the flagship programs at ISDI - the Master in Internet Business in Madrid, Barcelona and online.

Data & AI Module Program Manager

Regularly lecturing in advanced analytics topics at the Masters level and Executive programs in ISDI, an international institution focused on digital transformation.

Lecturer

VN Campaign Partners

Partner

Google

Developing insights and tools to empower our teams to work smarter and our clients to get the most out of Waze Ads. Leading analytical studies and strategy across North America for some of our largest advertising verticals (Fuel, Auto Parts & Services) as well as non-endemic verticals such as Entertainment. Creating scalable data solutions via daily use of SQL, BigQuery, Looker, CARTO, GSheets.

Analytical Lead, Waze

As a part-time facilitator, teaching engineers & analysts how to apply statistical methods to big data problems (Hypothesis Testing, T-Tests, Multiple Linear Regression, Logistic Regression, Centered Moving Average, Causal Impact, K-Means Clustering).

Master Faculty - Data Science I

Leading complex data projects, automation and optimization with our most advanced international clients cross-office.

Data Specialist

#IamRemarkable is an initiative empowering women & underrepresented groups to speak openly about their accomplishments in the workplace & beyond, thereby breaking modesty norms & glass ceilings. Workshops hosted for the UN World Tourism Organization, Amadeus, CARTO, IE Business School, LastMinute, Teleperformance, Google Campus - Digitalizadas.

#IamRemarkable Facilitator

Combining Google Search data with advanced analytics and Cloud tools to drive business impact. Working with a $xxx-million client portfolio.

Analytical Consultant

Nielsen

Maintaining data integrity and leading projects in data process optimization, as well as supporting a migration to Big Data technologies (Google Suite). Delivering both internal and external seminars and training courses around data topics.

Expert Engineer in Project Development and Big Data

Graduate Intern: Associate Systems Engineer

Graduate Fellow

Data Whisperer ♦ Analytics Specialist & Tech Enthusiast ♦ Join My #bookaweekchallenge

📚 Follow & join my #bookaweekchallenge
📍 Current resident of Málaga, Spain & native of Charlotte, North Carolina

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Greek-American currently living in Spain, helping companies harness the power of data. Driven analytics professional & strong supporter of lifelong learning. I hope to bring advanced data technologies to the masses & exploit the potentials of emerging technologies. I am bilingual & passionate about international relations. Avid reader (bibliophile) & traveler. With trips to 60+ countries & first-hand experience working in a variety of cultures & languages, I enjoy & thrive when working in an international environment.

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Alongside my corporate work, I am a regular international speaker supporting data analytics, emerging technologies & women in STEM. Some of my latest involvements include:
● Guest appearances on more than 30 podcasts such as SuperDataScience, Ken's Nearest Neighbors, How to Get An Analytics Job, Driven by Data, Humans of Data Science, Present Beyond Measure, LightsOnData & more.
● Interview features, guest lecturer, panelist and event host!
● Keynote speeches delivered regularly since 2018 across 15+ countries in-person, virtually spanning the globe, with audiences surpassing 1,000+. Top involvements include:
→ → The WPO Annual Conference 2022 in Montreal CA, "The Information Age: Digitized & Datafied"
→ → Bloom Tech Forum 2021, "Harnessing the Power of Data for a Better Tomorrow"
→ → The WPO Annual Conference 2021 in Nashville TN, “Data Science: A Double-Edged Sword"
→ → The World Knowledge Forum 2021, “The Information Age: Digitized & Datafied"
→ → The Global DIY-Summit 2021, "The Future of Retail: Data as a Differentiator"
→ → Africa Communications Week 2021, “The Shift toward Data-Driven Marketing & Communications” 
→ → DATAcated Conference 2021, “From zero to hero: a simple explanation of ML"
→ → DATAcated Conference 2020, “SQL isn't going anywhere"
→ → Quantic EMBA Conference 2020, “The Truth behind the Buzz- Emerging Data Technologies for the 21st Century"
→ → The Smart Future World Expo 2019 in Istanbul Turkey, "Harnessing the Power of Big Data"
→ → TechIE 2019 in Madrid Spain, "Big Data & ML Intro Workshop"
→ →  IE-UCOM Big Data Day 2018 in Yerevan Armenia, "Harnessing the Power of Big Data"

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For further opportunities (podcasts, events, etc.) in my interested fields, feel free to contact me directly.

The opinions stated here are my own, not those of my employers.

*Note that I may earn a small commission for sales through my affiliate links shared here.

Christina Stathopoulos, MSc

Wavicle Data Solutions

Lead Data Engineer

Globant

Senior Data Engineer

CRISIL Limited

Senior Analyst - Data Engineer

Quantiphi

Data Engineer

Tata Consultancy Services

System Engineer

Assistant System Engineer

Assistant System Engineer - Trainee

InsideSherpa

KPMG Data Analytics Consulting Virtual Internship

Monisha Enterprises

•	Data mining and Extraction Transformation Loading using R programming.
•	Data cleaning and processing for Data analysis done using Python using NumPy, pandas, SciPy, Scikit. Data modelling and prediction was done using algorithms – Logistic Regression, Time Series Forecasting, Clustering and Classification.
•	Data Visualization done using Tableau for generating reports to enhance the productivity of the products.
•	Built a Data Warehouse on MSSQL Server for ERP System of the enterprise to organize the data for future processing.

Data Analyst Intern

Open to Collaborate | Storyteller | Lead Data Engineer@Wavicle| Linkedin Top Voice 2024 | Globant | Linkedin Learning Instructor | 2xGCP & AWS Certified | LICAP'2022

Data Engineer with 7+ years of experience working on data engineering, analysis, and modelling projects for various domains, such as banking, healthcare, sales, and e-commerce. I leverage my strong statistical and analytical ability to explore business ideas and provide innovative solutions. 

Skilled with technologies and strong intellect with enthusiasm as an avid learner with challenging opportunities. I’m working as a LinkedIn Learning Instructor to bring change as a data evangelist.

Post Graduated in Master of Technology in Data Science and Engineering from Birla Institute of Technology and Science, Pilani in 2023, where I learned advanced concepts and techniques in data science and engineering, machine learning, cloud computing, and visualization. 

I hold multiple certifications in GCP, AWS, and Azure, and I enjoy working with cloud technologies and big data platforms.

I am passionate about data science and women empowerment, and I have been recognized as the 
- Linkedin Top Voice 2024 
- Data Science and Women Empowerment Thought Leader by Global AI Hub. 

I am also a proud participant of the LinkedIn Creator Accelerator Program 2022, where I share my insights and experiences with the data science community. 

I am open to collaborate and share ideas, or explore various opportunities in the data field.

Pooja Jain

Is your Database Architecture is Holding You Back? Let's Fix That.

Choosing the wrong database doesn’t just slow your queries—it compounds technical debt, burns budget, and limits scalability when you need it most.

As data engineers, we’re drowning in options: relational, document, graph, time-series, vector databases… the list goes on. 

👉 But here’s the reality: the database you choose today shapes your system’s capabilities for years.

Here's a well segregated reference to explore:
𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 - Organized data formatted in a structured form that's easy to analyse and stored in the form of rows and columns with definite relationships. 
→ This gets subdivided into OLTP(Relational) and OLAP(Analytics) based on the kind of transactions we prefer PostgreSQL, SQL, BigQuery and more. 

𝐒𝐞𝐦𝐢-𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 - It includes some variability and inconsistency within data that makes it difficult to store until you don't process and organize it. 
→ Having sources such as XML, JSON, dictionary, Entity Relationship Graph data and more. 
These can be further segregated as Document, Graph, Wide Columnar, Key-Value and others. 

𝐔𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 - Unorganized, unstructured data that can be in any raw format from social media post, chats, to images, videos, unstructured log files, PDF's and more. 
→ To handle these, we can access HDFS, S3, Blob Storage or Cloud Storage.

🔰Cloud providers offer various storage & database solutions:

𝗔𝗺𝗮𝘇𝗼𝗻 𝗪𝗲𝗯 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 (𝗔𝗪𝗦)- Amazon DynamoDB, S3, Amazon ElastiCache, Amazon Kinesis, Amazon Redshift and Amazon SimpleDB
𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 - Cloud Bigtable, Cloud Datastore, GCS, Firestore, BigQuery, Cloud SQL and Google Cloud Spanner
𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘇𝘂𝗿𝗲 - Azure Cosmos DB, Azure Table Storage, Azure Redis Cache, Azure Data Lake Storage, Blob Storage, Azure DocumentDB and Azure Redis Cache

Curious to know what should actually drive your decision?

→ Performance & Scalability – Can it handle your throughput today AND 10x growth tomorrow?
→ Data Model Fit – Does your data naturally map to rows, documents, nodes, or key-value pairs?
→ Integration Friction – How easily does it plug into your existing stack and workflows?
→ Total Cost of Ownership – License fees are just the start—factor in ops overhead, expertise required, and infrastructure costs.

The right database isn’t about chasing trends. It’s about matching architectural patterns to real-world constraints.

Image Credits: Rocky Bhatia 

What’s your go-to framework for database selection? Drop your approach below. 👇

Do you trust Data before Engineering it?

❌ No! Right?

👉  When deciding whether or not to trust data before engineering it here's what to consider:

→ Source of the data. Is the data from a reliable source?
→ Quality of the data. Is the data complete, accurate, and consistent?
→ Purpose of the engineering. What will the data be used for?
→ Risks of using untrustworthy data. What are the consequences of using data that is corrupted, incomplete, or inaccurate?

🔰 Here are some specific steps that can be taken to engineer data before it is used:
➤ Cleaning the data: This involves removing any errors or inconsistencies from the data.
➤ Formatting the data: This involves transforming the data into a format that is suitable for the intended use.
➤ Standardizing the data: This involves ensuring that the data is consistent in terms of its values and formats.
➤ Enriching the data: This involves adding additional information to the data, such as metadata or annotations.
➤ Encrypting the data: This involves protecting the data from unauthorized access.

But the this is the reality check for the hustle behind the scenes that data engineers face:

→ The Fun Part (10%):
- Dreaming Up New Pipelines
- Building New Pipelines

→ The Exciting Part (15%):
- Automating Processes
- Collaborating with Teams

→ The Crucial Part (20%): 
- Ensuring Data Quality
- Documenting Everything

→ The Never-Ending Part (50%): Literally drains your blood 🩸
- Debugging Existing Pipelines and Spending hours fixing unexpected issues 
- Refactoring Old Code to work with new data sources—an endless cycle.

→ The Patience-Testing Part (100%): That's never ending 🎭
- Dealing with Stakeholder Expectations
- Handling Schema Changes

➠ Data engineers who want to be successful in 2025-2026 and beyond, get your hands dirty leveraging below projects.

Beginner: 
ETL pipeline using Python and SQL by Ankit Bansal:
- https://lnkd.in/gTdCV9aJ
- https://lnkd.in/gxEYM3Bb

Intermediate:
- Data warehouse solution using Snowflake, dbt by Shashank Mishra 🇮🇳 : https://lnkd.in/gf-c5TR7
- Mr. K Talks Tech : https://lnkd.in/gt9JAkRt
- Snowflake project by Data Engineering Simplified : https://lnkd.in/gXRWyHpc
- Apache Spark project by Ankur Ranjan: https://lnkd.in/gt9JAkRt

Advanced: Implement a real-time streaming data processing
- Darshil Parmar : https://lnkd.in/ghiEsa7P
- Yusuf Ganiyu : https://lnkd.in/giYwJaCS

Cloud Projects:
- Microsoft Azure by Data Engineering Simplified - https://lnkd.in/gx3aqzKU
- Microsoft Azure by Sumit Mittal - https://lnkd.in/dX2yma5b
- Amazon Web Services (AWS) by Darshil Parmar - https://lnkd.in/gJg7KV-7
- Google Cloud by Anjan - https://lnkd.in/gjHbmCaM

System Design concepts with:
- Alex Xu - https://lnkd.in/gGdgJRDd
- Design Gurus - https://lnkd.in/gaphzp89

DataExpert.io handbook compiled by Zach Wilson -https://lnkd.in/gb4xBQJy

Image Credits: ByteByteGo - Alex Xu
#data #engineering

𝐀𝐈 ≠ 𝐌𝐚𝐠𝐢𝐜
Here’s my opinion,
Al models are built on data.

And guess what? That data is cleaned, shaped, and piped using... SQL & Python.

🧠 𝐓𝐡𝐢𝐧𝐤 𝐋𝐢𝐤𝐞 𝐚 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Before you build Al, you need:
 • Clean inputs
 • Reliable pipelines
 • Scalable infra

Python & SQL are the muscles behind that brain.

𝐖𝐡𝐲 𝐏𝐲𝐭𝐡𝐨𝐧?
𝗣 – 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Efficient at handling large-scale data processing using tools like Pandas, Dask, and PySpark.
𝗬 – 𝗬𝗶𝗲𝗹𝗱𝗶𝗻𝗴: Supports generators and lazy evaluation, which are crucial for memory-efficient data pipelines.
𝗧 – 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: Ideal for transforming raw data into structured formats using ETL (Extract, Transform, Load) processes.
𝗛 – 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴: Excellent at handling diverse data sources (CSV, JSON, Parquet, SQL, APIs, etc.).
𝗢 – 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Integrates well with workflow orchestration tools like Airflow, Prefect, and Luigi.
𝗡 – 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴: Supports building data pipelines across distributed systems and cloud platforms.

That's my definition of PYTHON. 
Whether you're building ETL pipelines, automating workflows, or integrating APIs, Python is a must-have tool in your data engineering toolkit.

𝐖𝐡𝐲 𝐒𝐐𝐋?
 • Universal language of data
 • Fast insights from massive datasets
 • Still irreplaceable in analytics & BI

Learn these SQL concepts:
- Basics: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT/OFFSET, DISTINCT.
- JOINs: INNER, LEFT, RIGHT, FULL OUTER, CROSS joins
- Subqueries: Nested and correlated
- Data Modeling: Indexing, normalization, data modeling.
- Common Table Expressions (CTEs): WITH clause for complex queries
- Window Functions: OVER, PARTITION BY, RANK, LEAD/LAG
- Normalization: 1NF through 5NF database design
- Views: Regular and materialized
- Stored Procedures: Business logic in database
- Transactions: ACID properties, isolation levels
- Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK
- Slowly Changing Dimensions: Types 1-6, tracking historical data changes
- Indexing: B-tree, bitmap, when to use indexes
- Query Optimization: Execution plans, performance tuning

👉Some of the easy to leverage free platforms to up-skill with Python/SQL:
- realpython.com
- pythonprogramming.net
- https://lnkd.in/gw45S8Hb
- https://datalemur.com/
- https://lnkd.in/gCDhJvA2
- https://lnkd.in/grqYWfGW

Here's an amazing SQL to Python cheatsheet to level up by Dawn Choo!

What else would you like to add?
#data #engineering #sql #python

Data Pipelines Don't Just Go One Way Anymore!
Modern Data Pipelines Are Messier Than You Think (In a Good Way)

As data engineers, we all start with the same gospel: ETL. 
But the flow of data is no longer a one-way street. 

We’ve moved from getting data in to making sure our clean insights can get out to the people who need them. 

Data engineering is now about the entire circuit.

𝗘𝗧𝗟: 𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 & 𝗟𝗼𝗮𝗱
→ What it is: Pull data from sources, transform it in transit, load it into your warehouse. 
→ Why? It made sense when computing was expensive and you wanted to minimize warehouse work. 
→ The Problem: It was slow and rigid. If the business model changed, you often had to rebuild the entire transformation step.

𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗘𝗧𝗟: 𝗧𝗵𝗲 𝗣𝗹𝗼𝘁 𝗧𝘄𝗶𝘀𝘁
→ What it is: This takes data FROM your warehouse and pushes it BACK to operational tools like your CRM or marketing platforms.
→ Why? Your data team built this beautiful warehouse with clean customer data. Meanwhile, sales is working with outdated info in Salesforce. Reverse ETL fixes that gap. It makes your insights actually useful instead of just sitting in dashboards.
→ The Loop: It closes the system. Data is no longer "done" when it lands in a BI tool; it’s done when it's back in the hands of the Sales Rep or the Marketing Automation tool.

𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗲𝗳𝗮𝘂𝗹𝘁: 𝗘𝗟𝗧 & 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴
→ ELT (Extract, Load, Transform): Load the source data first, then transform it inside the warehouse. Cloud tools like BigQuery and Snowflake made this popular.
→ Real-Time Streaming Tools like Apache Kafka and Estuary Flow move data instantly, not on overnight schedules. Critical for live dashboards or anything where seconds matter.
→ CDC (Change Data Capture) Streams only what changed instead of dumping everything repeatedly. Database updates hit your analytics in seconds, not hours.
→ Zero-ETL Integrations Skip traditional ETL entirely with direct app-to-warehouse connections. Less infrastructure, less maintenance.
→ Modern Integration Platforms Fivetran, Hightouch and similar tools offer drag-and-drop connectivity without code. Many handle both regular ETL and reverse ETL.

If you're confused, which one shoud you pick?
👉 Go ELT if you're on cloud warehouses and your team knows SQL/dbt.
Use CDC/Streaming for fresh data/operational analytics or AI.
➕ Add Reverse ETL when business teams need warehouse data in their daily tools.
🪧 Stick with ETL only if you have on-premises requirements or need mature enterprise governance.
🔥 Most companies end up with a mix. The key is picking tools that work together and match where your team actually is.

Start simple. Get one pipeline working. Then expand.
Image Credits: Shanoj Kumar V

Ready to rethink your ETL setup or try out reverse ETL tricks? 
👇Share your favorite tools, biggest headaches, or clever hacks in the comments. 
♻️ Save, Repost & stay tuned with me(Pooja) for more on Data Engineering!

Gain Confidence by doing things instead of ➖

Planning
Learning
Scheduling
Researching
Overthinking

Do you agree?

The Symbiotic Relationship: AI × Data Engineering × Data Science

Let's not assume that data engineering, AI, and data science aren't separate lanes. 

It's more like a feedback loop where each one makes the others better.

1️⃣ 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗙𝘂𝗲𝗹 𝗔𝗜 & 𝗦𝗰𝗶𝗲𝗻𝗰𝗲
Your data pipelines are the foundation. Without clean, reliable data flowing through, nothing else works.

→ Engineering to Science: Data Engineers build the high-quality, structured pipelines that deliver the training data.
→ Example: Making sure all customer records are deduplicated and financial data is validated before it hits the Data Scientist's workspace. Bad pipeline means a garbage model.

2️⃣ 𝗔𝗜 𝗣𝗼𝘄𝗲𝗿𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 
AI isn't just the end product. It's becoming a tool that helps engineers build better pipelines faster.

→ AI to Engineering: AI tools automate the tedious, repetitive work of the data team itself.
→ Example: Using machine learning models to automatically detect anomalies in a production data stream or applying AI to auto-generate documentation for complex ETL jobs.

3️⃣ 𝗦𝗰𝗶𝗲𝗻𝗰𝗲-𝗗𝗿𝗶𝘃𝗲𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
Data scientists aren't just consumers of data. Their insights tell engineers what actually matters and where to focus.

→ Science to Engineering: Data Science insights guide the optimization of data flows and storage.
→ Example: An analysis shows that 80% of business value comes from five specific data fields. The Data Engineer then prioritizes making those five fields near real-time, while slowing down less-critical flows to save cost.

4️⃣ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗼𝗽
This is where it gets interesting. Once everything's connected, the system starts getting smarter on its own.

→ Interconnected Flow: The performance of the live AI models provides feedback directly to the Data Infrastructure.
→ Example: A deployed prediction model shows a specific data source is drifting in quality. The system alerts the Data Engineer to rebuild that source's validation checks, leading to a better pipeline, which leads to a better model.

None of these roles shine alone, here's my 2 cents - 
📍Your data pipelines only matter if someone's using the data. 
📍Your AI models are only as good as the data feeding them. 
📍Your data science insights are worthless if engineering can't implement them.

#data #engineering #AI #datascience

𝗛𝗶𝗿𝗶𝗻𝗴 𝗔𝗹𝗲𝗿𝘁: 𝗚𝗖𝗣 𝗗𝗲𝘃𝗢𝗽𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿
Passionate about automation, scalability, and cloud innovation?
This opportunity might just be the perfect next step for you!

Location: Chennai/ Coimbatore/ Remote
Experience Level: 5–10 years
Availability: Immediate Joiners Preferred

About the Role
We at Wavicle Data Solutions are looking for a skilled GCP DevOps Engineer to design, implement, and manage cloud infrastructure and CI/CD pipelines on Google Cloud Platform (GCP). 
You’ll be responsible for enabling seamless deployments, optimizing infrastructure, and ensuring reliability across environments.

Key Responsibilities
·       Design, deploy, and manage infrastructure using GCP services such as Cloud Run, Compute Engine, GCS, BigQuery, Secret Manager, Pub/Sub, Dataflow, GKE, Cloud Build, and Cloud Deploy.
·       Develop and maintain Infrastructure as Code (IaC) using Terraform.
·       Build and maintain CI/CD pipelines using Jenkins or GitHub Actions.
·       Manage containerization with Docker, GKE, and Artifact Registry.
·       Configure and optimize monitoring, logging, and alerting.
·       Implement networking configurations for secure and scalable setups.
·       Automate operational workflows with Python, Bash, and Airflow/Astronomer.

What are we Looking For?
·       5–10 years of relevant DevOps experience, with 3+ years on GCP.
·       Hands-on experience in Terraform, Jenkins, GitHub Actions, Docker, Kubernetes, and GKE.
·       Solid understanding of GCP networking, security, and monitoring.
·       Scripting proficiency in Python, Bash, and SQL.
·       Excellent communication and collaboration skills.

Bonus Points If You Have
·       GCP Professional DevOps Engineer or Cloud Architect certification.
·       Experience in data-driven or analytics-focused environments.
·       Exposure to multi-cloud or hybrid architectures.

If you are interested kindly share your resume at -pooja.jain@wavicledata.com

#hiring #jobs #data #engineering #cloud #devops

Wish you and your family a very Happy Diwali.

May this celebration lightens up your lives with more happiness and better opportunities!!

taplio

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Christina Stathopoulos, MSc

Pooja Jain

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Pooja Jain's LinkedIn Performance

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Performance ScorePooja Jain's LinkedIn Performance