Data Engineer

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. 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. PREFERRED QUALIFICATIONS • 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. 





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