About This Job
At Epic, we challenge what’s expected and push boundaries to create real impact. Driven by innovation and efficiency, we’re always finding smarter ways to deliver more value to our people and our customers.
We act as Challengers, stay Authentic to our purpose, and are Passionate about delivering a Great Network for Great Value. United as One Team, we collaborate, support one another, and succeed together.
Join Epic to be part of a bold, forward‑thinking environment where your ideas matter, your growth is supported, and your work makes a difference.
Role Overview
We are seeking a talented and motivated Data Engineer to join our dynamic Data Engineering team.
In this role, you will play a key part in designing, building, and maintaining Epic’s data infrastructure, enabling reliable analytics, reporting, and future AI‑driven solutions.
You will work closely with analysts, engineers, and business stakeholders to understand data needs and translate them into scalable, high‑quality data solutions. This role requires strong technical expertise, a problem‑solving mindset, and a passion for working with modern data platforms and technologies.
The position also involves participation in a 24×7 on‑call roster, supporting business‑critical systems and ensuring service continuity outside standard working hours.
Key Responsibilities
- Develop a strong understanding of Epic’s diverse data sources to design, build, and maintain reliable data pipelines and processing frameworks.
- Collaborate with data analysts and stakeholders to gather requirements and translate them into effective technical solutions.
- Design, build, and maintain the data warehouse, data lakehouse, and related data repositories.
- Architect data pipelines around built-in provenance, stable record identities, idempotent operations, explicit reprocessing rules, and observable, replayable failures.
- Optimise existing pipelines and create new ones to ensure performance, scalability, and reliability.
- Ensure high data quality and integrity through validation, cleansing, and monitoring processes.
- Implement and maintain data security, governance, and privacy best practices across all systems.
- Learn and gain hands‑on experience with the existing data warehouse and analytics environment.
- Stay up to date with advancements in data engineering, analytics platforms, and AI foundations to help build future‑ready solutions.
Technology Stack
Programming & Scripting
- Linux shell scripting, Python, Bash, PL/SQL
ETL / ELT & Data Processing
- Airflow, dbt, dlt, kafka, pulsar, PySpark, Dremio, Talend
Databases & Storage
- Oracle, PostgreSQL, MariaDB
- Iceberg tables
- AWS S3 and modern data lake architectures
- Columnar Databases
Development & Collaboration Tools
- GitLab, Docker, VS Code
- JIRA, Confluence, SharePoint
BI & Reporting
- Familiar with SAP BusinessObjects or Power BI
Skills, Behaviour & Competencies
- Strong analytical skills with the ability to identify requirements and design effective solutions.
- Self‑starter with the ability to work independently and take initiative.
- Collaborative team player who thrives in cross‑functional environments.
- Fast learner with the ability to quickly acquire new technical skills and knowledge.
- Strong planning and prioritisation skills to manage multiple tasks and deadlines.
- Creative problem‑solver with a continuous improvement mindset.
- Genuine curiosity for data and enthusiasm for laying the foundations for AI‑enabled technologies.
Experience & Qualifications
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in a Data Engineer or similar technical role.
- Strong proficiency in SQL and Python.
- Hands‑on experience with data warehousing and data lake technologies (e.g. Dremio, Snowflake, Databricks, AWS S3).
- Hands-on experience with high-performance, open-source columnar databases designed specifically for OLAP (Online Analytical Processing) and real-time analytics E.g. Clickhouse or Apache Doris.
- Experience with cloud platforms and containerisation/orchestration technologies such as Kubernetes.
- Experience developing with and deploying modern data tools (e.g., Airflow, dbt, dlt, Python frameworks) using GitOps continuous delivery (e.g., Argo CD, Flux) in Kubernetes environments.
- Solid understanding of data modelling, data warehousing, and data lakehouse concepts.
- Experience with Linux operating systems is considered an asset.
- Knowledge of telecommunications systems and database administration is a plus.
- Strong communication skills with the ability to collaborate effectively across technical and non‑technical teams.