Data Engineer

We are hiring a hands-on Data Engineer to join our India-based team and help build the next generation of our data delivery platform.

• Build and operate pipelines: Design, build, and optimize robust ETL/ELT pipelines that move and transform millions of daily data points reliably and efficiently.
• Work hands-on with the open-source stack: Build and maintain production data workflows using tools such as dbt, ClickHouse, and open-source orchestration frameworks (Airflow, Dagster, or similar).
• Engineer and tune databases: Write and optimize complex SQL across SQL Server and Postgres — schema design, indexing, performance tuning, query optimization, and root-cause analysis.
• Support platform modernization: Contribute to the hands-on migration away from Azure Databricks toward a more open, flexible, AWS-based stack, minimizing disruption to high-volume daily data delivery.
• Prototype and evaluate: Help build proofs-of-concept to benchmark new tools and patterns, and feed clear results back into the team’s adoption decisions.
• Work with AI-assisted tooling: Use AI coding assistants and well-crafted prompts (e.g., GitHub Copilot, Claude, ChatGPT) to accelerate pipeline development, SQL generation, debugging, testing, and documentation — always reviewing and validating the output before it ships.
• Safeguard data quality and reliability: Implement testing, validation, monitoring, observability, and CI/CD practices so data stays accurate and pipelines stay healthy at scale.
• Integrate across systems: Understand the systems upstream and downstream of your pipelines, from ingestion through to client-facing platforms, to ensure clean, end-to-end data delivery.
• Collaborate across a distributed team: Partner with engineers in North America and India, participate in code reviews, and learn the nuances of the Advertising and Market Research domain.

• Experience: 7–8+ years in data engineering / ETL, with a strong track record as a hands-on engineer building and operating production data pipelines.
• Core databases: Strong SQL and RDBMS skills with solid, hands-on experience in SQL Server and Postgres (schema design, performance tuning, complex query optimization).
• Open-source and modern stack: Hands-on experience with tools such as dbt, ClickHouse, and open-source pipeline / orchestration tools (e.g., Airflow, Dagster), with the judgment to choose the right tool for the job.
• Programming: Strong proficiency in Python (or a similar language) for building and automating data pipelines.
• AI-assisted development: Hands-on experience using AI coding assistants and effective prompting techniques to work more efficiently, with the judgment to verify, test, and refine AI-generated code and queries.
• Cloud: Hands-on experience with a major cloud platform; AWS strongly preferred, as we are standardizing on AWS as we move off Azure Databricks.
• Engineering fundamentals: Comfortable with Git, code reviews, and writing tested, maintainable, well-documented code.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• Bonus Points (Nice to Haves): Data warehousing: Experience with cloud data warehouses or lakehouse patterns (Snowflake, BigQuery, Redshift, or similar). Streaming and real-time: Experience with streaming / real-time data technologies such as Kafka. Infrastructure: Familiarity with containerization (Docker, Kubernetes) and infrastructure-as-code (e.g., Terraform). Domain expertise: Previous experience in Advertising, Media, or Market Research.