RELX International
Data Engineer
- Compensation
- Not disclosed
- Location
- Manila, Philippines
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related discipline, or equivalent practical experience.
- Typically 2-5 years of experience in data engineering, database development, software engineering, analytics engineering, or a related role.
- Working proficiency in SQL and Python.
- Experience developing or supporting ETL/ELT pipelines.
- Hands-on experience with AWS or another cloud-based data platform.
- Experience working with relational databases such as PostgreSQL, Microsoft SQL Server, or Oracle.
- Experience processing structured and semi-structured formats such as JSON, CSV, and Parquet.
- Familiarity with API ingestion, authentication, pagination, batch processing, incremental loading, and data validation.
- Familiarity with Git, pull requests, code reviews, testing, and deployment workflows.
- Ability to investigate data issues and communicate progress, risks, and blockers clearly.
- Ability to collaborate with technical and business stakeholders in a global environment.
- Strong problem-solving, organizational, documentation, and communication skills.
- Ability to work 8 hours of overlap with (Eastern/Central) US business hours
- Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives.
Nice to have
- Experience with GA4 data models or APIs.
- Familiarity with marketing attribution, campaign tracking, and UTM structures.
- Experience working with paid-media or social-media APIs.
- Familiarity with AWS S3, Glue, Athena, and CloudWatch.
- Familiarity with Databricks, PySpark, Airflow, or similar data-processing and orchestration technologies.
- Experience working in an Agile environment.
Full description
Job Description
ACCOUNTABILITIES
- Develop, test, and maintain ETL/ELT pipelines that ingest structured and semi-structured data from third-party sources, including GA4, paid media, social media, and other marketing platforms.
- Build and support API and batch-ingestion workflows, including pagination, rate-limit handling, retries, and incremental loads.
- Integrate web traffic, campaign, engagement, and related business data into the AWS data lake.
- Transform source data into consistent, reusable datasets using established standards for data types, normalization, deduplication, and validation.
- Monitor scheduled pipelines and troubleshoot data-quality, performance, schema, and processing issues.
- Implement data-quality checks and communicate failures, risks, and blockers to the appropriate team members.
- Work with AWS data services such as S3, Glue, Athena, and CloudWatch, or equivalent cloud technologies.
- Use Git-based development practices, including branches, pull requests, peer reviews, and controlled deployments.
- Perform unit testing and source-to-target validation for pipeline changes.
- Maintain technical documentation for data sources, mappings, transformations, business rules, and operational procedures.
- Collaborate with Sr. Engineers, reporting analysts, and business stakeholders to translate requirements into technical tasks.
- Implement established data-governance, privacy, consent, access, and retention requirements.
- Participate in Agile planning, estimation, demonstrations, and retrospectives.
- Responsibly use approved enterprise AI tools while validating generated code and protecting company and customer data.
QUALIFICATIONS
- Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related discipline, or equivalent practical experience.
- Typically 2-5 years of experience in data engineering, database development, software engineering, analytics engineering, or a related role.
- Working proficiency in SQL and Python.
- Experience developing or supporting ETL/ELT pipelines.
- Hands-on experience with AWS or another cloud-based data platform.
- Experience working with relational databases such as PostgreSQL, Microsoft SQL Server, or Oracle.
- Experience processing structured and semi-structured formats such as JSON, CSV, and Parquet.
- Familiarity with API ingestion, authentication, pagination, batch processing, incremental loading, and data validation.