Data Engineering Manager - Enterprise Data

Role & responsibilities

We are recruiting for a technically strong, people-centric Data Engineering Manager to lead a high-impact AI & Data engineering team based in India. This group will build the data platform and products to support the analytics, insights and data science needs for our core subscription business. Data has always been at the heart of the Economist Group, and ultimately we aim to help the business make data driven judgements based on real time, actionable insights

How you will contribute in this role:

• Work with data product managers, analysts, and data scientists to build data and ML pipelines, data warehouse/data lake-house for analytics, reporting and prediction.
• Build pipelines and data lakehouse/warehouse with data feeds/APIs using Lakehouse, Snowflake, Apache Airflow, Amazon EMR on Snowflake and AWS data lakehouse.
• Work with data quality and observability tools, CI/CD frameworks to maintain data engineering excellence.
• Work on data modelling, lineage and data governance
• Manage and support a team of engineers through regular 1:1s, coaching, performance feedback, and career development. Foster an inclusive, growth-oriented team culture
• Contribute to engineering strategy and practices. Shape processes, team rituals, and tooling that enable your team to deliver high-quality code efficiently and safely
• Take full ownership of systems in your domain, ensuring observability, uptime, performance, documentation, and operational readiness
• Understand the productivity of your teams and the factors affecting productivity and intervening when required

Preferred candidate profile

• 8+ years of professional experience of designing, building data pipelines and managing data platforms (including ETL, ELT and lambda architectures), using technologies such as Apache Airflow, Amazon Athena, AWS Glue, Amazon EMR, or other equivalent.
• Expert level knowledge of both SQL and programming (Python, Scala, or Java)
• Experience with data technologies such as object storage (s3, hdfs), lakehouse (Iceberg or equivalent), data warehousing (Snowflake or equivalent), orchestration (Airflow or equivalent), data processing frameworks (Pyspark, pandas or equivalent) data modelling, lineage and governance tools.
• Professional experience in cloud platforms (AWS strongly preferred), such as serverless functions, API gateway, relational and NoSQL databases, and caching.
• Experience with IaC, CI/CD, containerization, and DevOps.
• Experience in working in teams with data scientists and ML engineers, for building ML pipelines for recommendation, customer lifetime value and propensity models.
• An advanced degree in software / data engineering, computer / information science, or a related quantitative field or equivalent work experience.
• Strong verbal and written communication skills and ability to work well with a wide range of stakeholders.
• Strong ownership, scrappy and biased for action.