Data Engineer

Swedium Global Services Pvt. Ltd.

Swedium Global is a growing System Engineering and Solution Company offering services such as Semiconductor Engineering R&D Services, Embedded Systems Development, Custom Application Software Development, Web and Cloud Application Development, Testing Services, Consultancy, and Outsourcing services to clients across the globe through both onsite and offshore business models.

Swedium Global has a presence in Sweden, Finland, Poland, the Czech Republic, and India.

We are looking for a Data Engineer for our client Truecaller.

Assignment Description

Profile/Role: Data Engineer
Client: Truecaller
Location: Stockholm, Sweden
Work Model: Remote or Hybrid
Workload: 100%
Seniority: 5+ years
Language: English – Proficient

Domain: Data Engineering / Data Infrastructure / Analytics Platform

About the Role

We are looking for a Data Engineer to join the Data Infrastructure Team, responsible for building and maintaining a scalable, robust, and self-service analytics platform.

This is a hands-on role focused on data infrastructure, large-scale data ingestion, ETL pipelines, production data platforms, cloud technologies, orchestration, and streaming use cases.

You will work closely with Data Engineers, Data Scientists, Data Analysts, and other technical teams to build, operate, and continuously improve reliable data platforms capable of handling billions of events daily.

Key Responsibilities

• Maintain and improve data ingestion pipelines to reliably process billions of events daily within defined SLAs.
• Build, maintain, and optimize complex ETL/data pipelines.
• Support teams in building and optimizing their complex data pipelines.
• Work closely with teams to identify and resolve data platform pain points and technical challenges.
• Develop new tools and frameworks to improve the data platform.
• Work closely with Data Scientists and Data Analysts to support their production workloads.
• Contribute to large-scale projects such as ML platforms and streaming use cases.
• Build, scale, and operate production-grade data platforms and data infrastructure.
• Ensure platform reliability, observability, performance, and self-service capabilities.
• Establish and promote best practices around software and data development.
• Take ownership of technical problems and drive them through to resolution.

Core Technical Skills — Must Have

• Strong experience in Data Engineering
• GCP / Google Cloud Platform
• GKE / Google Kubernetes Engine
• Apache Airflow
• Apache Spark
• Scala
• Python
• Complex ETL/ELT pipelines
• Large-scale data processing
• Data ingestion
• Production-grade data platforms
• Kubernetes
• CI/CD
• Jenkins
• Git
• Data platform reliability and observability
• Performance optimization
• Production operations

Critical Production Experience

Candidates must demonstrate hands-on experience building, scaling, and operating production-grade data platforms or data infrastructure, including reliability, observability, performance, and self-service capabilities.

Candidates must demonstrate operational ownership of self-hosted, open-source data infrastructure running on GKE.

A relevant example is operating Apache Airflow in production, including:

• Airflow version upgrades
• Scheduler tuning
• Executor tuning
• Production troubleshooting
• Performance optimization
• Reliability and observability

Experience limited to deploying workloads onto a platform operated by another team is likely insufficient.

Preferred Skills

• Apache Kafka
• Messaging systems
• Kubernetes
• Streaming platforms
• BigQuery
• Amazon Redshift
• ML platforms
• Cloud data platforms
• Data observability
• Data reliability
• Performance optimization