Data Engineer

Job Summary and Key Accountabilities

The Data Operations Specialist serves as a crucial link between marketing data sources and the analytics solutions that depend on them. The role requires the ability to understand different data structures and design reliable data ingestion, transformation, modelling and storage solutions that turn raw marketing data into trusted, reusable data products for analysts and business stakeholders.

The role also requires hands-on experience working with cloud data warehouses, APIs, Python and modern transformation frameworks such as dbt or Dataform. Exposure to building AI-enabled data or analytics solutions is an advantage.

Key Accountabilities and Responsibilities:

• Design, develop, and support reliable data pipelines, warehouses, data models, and reusable data products to support marketing performance analysis and reporting.
• Build and maintain data ingestion from marketing, CRM, advertising and other business systems using managed connectors where appropriate.
• Develop custom API-based data extraction when managed connectors are unavailable or insufficient, including authentication, pagination, incremental extraction, error handling and loading data into the cloud data warehouse.
• Develop and maintain modular SQL transformation workflows using dbt, Dataform, or equivalent transformation frameworks.
• Use Python for API integration, data processing, validation, automation and reusable data-engineering workflows.
• Translate business and reporting requirements into scalable technical designs and reusable data models.
• Design and maintain dimensional models, facts, dimensions and analytical data marts.
• Implement data-quality checks, testing, documentation and monitoring to ensure production data products remain reliable.
• Use Git and code-review practices to manage changes to production data transformations and engineering workflows.
• Troubleshoot existing data pipelines and transformations to identify and resolve recurring data-quality and reliability issues.
• Partner with business stakeholders, Data Analysts, upstream platform teams and downstream data consumers to understand requirements and data dependencies.
• Build proofs of concept and reusable engineering patterns that improve scalability, maintainability and delivery efficiency.
• Where appropriate, explore and develop AI-enabled data or analytics solutions that automate repetitive analytical, reporting, enrichment or data-quality tasks.
• Serve as a trusted technical resource on reliable and scalable data solutions.

Required Qualifications

• 3+ years of experience in analytics engineering, data engineering, data operations, or a related hands-on data role.
• Bachelor's degree in Information Technology, Engineering, Data Analytics, or a related discipline/ equivalent practical experience.
• Advanced SQL skills to build and optimize reusable transformations, facts, dimensions, and analytical data models.
• Strong hands-on Python skills for data integration, API interaction, data processing, validation, and automation.
• Hands-on experience extracting and processing data from REST APIs, including authentication, pagination, JSON responses, and error handling.
• Hands-on experience with dbt, Dataform, or an equivalent SQL-based transformation framework, including modular modelling, dependency management, testing, and version-controlled development.
• Hands-on experience with GCP and BigQuery, including working with cloud-based datasets, transformations, and analytical data models.
• Experience with managed ingestion tools such as Stitch, Fivetran, or equivalent for extracting and loading data from source systems.
• Experience working with marketing, CRM, and marketing-automation platforms such as Salesforce, HubSpot, Marketo, Pardot, or equivalent, with an understanding of their underlying data structures.
• Strong knowledge of schema design and dimensional data modelling.
• Working knowledge of Git and code-review workflows for managing changes to production data solutions.
• Strong understanding of data-quality, testing, documentation, monitoring, and other modern data-engineering practices required to maintain reliable production data products.
• Proven experience translating business and reporting requirements into scalable technical data solutions.
• Strong command of English and comfortable participating in client-facing meetings with US stakeholders.
• Excellent writing, communication and presentation skills.

Preferred / Good-to-Have Qualifications

• Familiarity with BI and data visualization tools such as Looker Studio, Power BI, or Tableau.
• Hands-on experience developing AI-enabled data or analytics solutions that embed LLMs such as Gemini or equivalent into repeatable workflows, with practices such as structured outputs, grounding, validation, human review or monitoring.
• Experience building reusable frameworks, templates, or proofs of concept that improve engineering efficiency and scalability.