Data Engineer II

Responsibilities

• 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.

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

• 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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