Data Engineer – Media Analytics

About the Role

We are looking for a Data Engineer to design, build, and optimize scalable data pipelines and infrastructure that power media and marketing analytics. This role is primarily engineering- focused but requires collaboration with analytics teams to enable insights through well-structured

data models and visualization-ready datasets. Experience with Python, SQL, and cloud platforms is essential, while familiarity with Power BI/Tableau and basic data analysis is a plus.

Key Responsibilities

• Design, develop, and maintain ETL/ELT pipelines using orchestration tools (Apache Airflow, Cloud Composer, or similar).
• Build scalable, cloud-native data architectures on GCP and/or Azure, including compute, storage, networking, and IAM.
• Develop and manage API connectors for ingesting data from marketing platforms (Google Ads, Meta Ads, DV360, CM360, LinkedIn Ads) and internal systems.
• Implement CI/CD pipelines for automated deployment, testing, and monitoring of data workflows.
• Build and optimize data models for analytics and reporting, ensuring performance and reliability.
• Collaborate with data analysts and business teams to translate requirements into engineering solutions and prepare datasets for BI tools (Power BI, Tableau).
• Perform basic data validation and analysis to ensure data accuracy and usability for dashboards.
• Implement data quality checks, monitoring, and alerting for pipeline health and integrity.
• Ensure data governance, security, and compliance across all engineering outputs.
• Maintain code quality through GitHub workflows, version control, and documentation.

Required Skills

• 3–6 years of experience as a Data Engineer or similar role.
• Strong expertise in cloud platforms (GCP and/or Azure) including compute, storage, networking, IAM, and serverless services.
• Proficiency in Python for ETL, API integrations, and automation.
• Strong SQL skills and experience with analytical warehouses (BigQuery, Snowflake, Redshift, Synapse).
• Familiarity with data visualization tools (Power BI, Tableau) and ability to prepare datasets for reporting.
• Hands-on experience with orchestration tools (Airflow, Cloud Composer, Prefect, ADF).
• Knowledge of ETL/ELT best practices, data modelling, and pipeline optimization.
• Experience with GitHub, version control, and CI/CD pipelines.
• Good understanding of networking fundamentals (VPCs, subnets, firewalls, private endpoints).
• Basic to intermediate cloud security knowledge (IAM policies, encryption, secrets management).
• Experience building and deploying API connectors (REST, GraphQL, OAuth).
• Familiarity with dbt for transformation and modelling.

Good to Have

• Experience with APIs such as Google Ads, Meta Ads, DV360, CM360, LinkedInAds.
• Understanding of marketing KPIs, attribution models, and campaign performance metrics.
• Exposure to real-time streaming (Kafka, Pub/Sub, Event Hub).
• Familiarity with containerization (Docker, Kubernetes) and IaC (Terraform).
• Experience with data quality frameworks (Great Expectations, Monte Carlo, Soda).
• Additional cloud security exposure (network security groups, logging/monitoring, vulnerability scanning).
• Exposure to ML engineering workflows (feature engineering, model deployment) and tools like Vertex AI, Azure ML, or SageMaker. Personal Attributes
• Strong problem-solving skills and attention to detail.
• Collaborative mindset with ability to work in cross-functional teams.
• Ability to translate complex requirements into scalable engineering solutions.