hackajob Recenzje 1378

Think its a data harvesting company . They advertised for a role on linkedin , it took me to the site and there was NO option to apply after extensive creation of profile.

Hackajob made my job search in the UK simple and effective. The platform connected me directly with top employers, and the process was fast, transparent, and well-organized. Their team provided clear communication and support at every stage, from application to offer. I’m very satisfied with the experience and would recommend Hackajob to any tech professional looking for new opportunities.

Good app overall

I was invited to apply for multiple opportunities. I applied to the roles and submitted application from hackajob.
My all applications got rejected. If they had to reject my application why I was invited. Complete waste of time.

Tried my best.

Very very good

Links didn't work and you ruined a great opportunity for my employments

I got the job

Thanks for sharing genuine job for me

It advertise roles that for companies that aren't even listed on the site (unusalble site). Tech product manager from Civica. On the company site it is not advertised, in hackajob Civica does not exists. Do not waste people time please!

great experience with site

In my month of use I had 2 companies reach out. While I found a role, I found the use of hackajob confusing and lacked a clear profression track notification system. I feel this portal needs more development.

The follow up-email reminders is a great initiative

I have over four years of experience as a Data Engineer, working across enterprise environments such as Uber, KPMG, and Trigent. My expertise lies in designing, building, and scaling both real-time and batch data pipelines, with a strong focus on Python, PySpark, SQL, Kafka, and Apache Airflow.
At Uber, I architected and optimized scalable ETL pipelines, which reduced batch processing times by nearly 30% and improved pipeline reliability by over 40%. I led migrations of critical workloads to AWS Glue and Redshift, achieving cost reductions and better scalability. I also built robust data models for Power BI and Tableau, enabling faster report generation and actionable insights for cross-functional teams. Additionally, I implemented end-to-end data governance and compliance protocols, ensuring pipelines were fully audit-ready and GDPR/SOX compliant.
During my internship at KPMG, I focused on automating ETL workflows and integrating data from multiple sources, including Azure Data Factory, S3, and PostgreSQL. I also developed ML pipelines using MLflow and Python, cutting deployment time in half while maintaining production monitoring. My work there improved reporting efficiency and supported executive decision-making through accurate, timely dashboards.
At Trigent, I designed and deployed large-scale ETL pipelines and migrated legacy systems to AWS Cloud and Redshift, increasing system uptime and reducing infrastructure costs. I implemented star and snowflake schemas for enterprise data warehousing and built real-time streaming pipelines with Kafka, enabling efficient event processing under peak loads. I also focused on improving data quality, observability, and CI/CD automation, ensuring high reliability and accuracy for enterprise datasets.
Across all roles, I have collaborated closely with data science, DevOps, and analytics teams to deliver production-grade, low-latency data systems that power business intelligence, ML models, and operational decision-making. I take pride in not just handling technical challenges, but also translating them into measurable business impact.

yes receiving opportunities is very good