Data Engineer - ETL

Key Responsibilities

• Involved in entire development lifecycle, from brainstorming ideas to implementing elegant solutions to obtain data insights.
• Gather requirements, model and design solutions to support product analytics, business analytics and advance data science.
• Design efficient and scalable data pipelines using cloud‑native and open source technologies.
• Develop and improve ETL/ELT processes to ingest data from diverse sources.
• Work with analysts, understand requirements, develop technical specifications for ETLs, including documentation.
• Support production code to produce comprehensive and accurate datasets.
• Automate deployment and monitoring of data workflows using CI/CD best practices.
• Promote strategies to improve data modelling, quality and architecture.
• Participate in code reviews, mentor junior engineers, and contribute to team knowledge sharing.
• Document data processes, architecture, and workflows for transparency and maintainability.
• Work with big data solutions, data modelling, understand the ETL pipelines and dashboard tools.

Required Qualifications

• 4+ years relevant industry experience in a data engineering role and graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field.
• Proficiency in writing SQL queries and knowledge of cloud‑based databases like Snowflake, Redshift, BigQuery or other big data solutions.
• Experience in data modelling and tools such as dbt, ETL processes, and data warehousing.
• Experience with at least one of the programming languages Python, C++ or Java.
• Experience with version control and code review tools such as Git.
• Knowledge of latest data pipeline orchestration tools such as Airflow.
• Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure‑as‑code tools (e.g., Docker, Terraform, CloudFormation).
• Familiarity with data quality, data governance, and observability tools (e.g., Great Expectations, Monte Carlo).
• Experience with BI and data visualization tools (e.g., Looker, Tableau, Power BI).
• Experience working with product analytics solutions (Amplitude, Mixpanel).
• Experience working on mobile attribution solutions (Appsflyer, Singular).
• Experience working on a mobile game or a mobile app, ideally from early stages of the product life cycle.
• Experience working in an Agile development environment and familiar with process management tools such as JIRA, Target process, Trello or similar.

Nice to Have

• Familiarity with data security, privacy, and compliance frameworks.
• Exposure to machine learning pipelines, MLOps, or AI‑driven data products.
• Experience with big data platforms and technologies such as EMR, Databricks, Kafka, Spark.
• Exposure to AI/ML concepts and collaboration with data science or AI teams.
• Experience integrating data solutions with AI/ML platforms or supporting AI‑driven analytics.

Seniority level

Mid‑Senior level

Employment type

Full‑time

Job function

Engineering and Information Technology

Industries

Computer Games

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