Staff Analytics Engineer - Data Analytics Team, Hyderabad

We develop and support strategic data & analytical products combining advanced analytics, statistics, experimentation, machine learning, and scalable data engineering to generate actionable insights that influence activation, monetization, and revenue growth across global markets. Working with large-scale behavioral, subscription, authentication, and payment data, we solve complex business problems and drive data-informed decision-making across the organization. As a Staff Analytics Engineer, you will be responsible for data pipeline, data strategy, and data visualization-related efforts for the Commerce & Growth Analytics team at WBD. You will manage projects from requirements gathering to planning to implementation of full-stack data solutions (pipelines to data tables to visualizations). You will work closely with cross-functional partners to ensure that business logic is properly represented in the semantic layer and production environments. Design and implement data models that support flexible querying and data visualization. Partner with Commerce and Growth stakeholders to understand business questions and build out advanced analytical solutions. Build frameworks that multiply the productivity of the team and are intuitive for other data teams to leverage. Create systematic solutions for solving data anomalies: identifying, alerting, and root cause analysis. Work proactively with stakeholders to ready data solutions for new product and/or feature releases, with a keen eye for uncovering and troubleshooting any data quality issues or nuances. Identify and explore new opportunities through creative analytical and engineering methods. Create new or optimize existing analytics development standards and best practices. Mentor and provide guidance to junior data analytics engineers on the team. Bachelor's degree, MS, or greater in a quantitative field of study (Computer/Data Science, Engineering, Mathematics, Statistics, etc.). 9+ years of relevant experience in business intelligence / data engineering. Expertise in writing SQL (clean, fast code is a must) and in data-warehousing concepts such as star schemas, slowly changing dimensions, and ELT/ETL. Experience in transforming flawed/changing data into consistent, trustworthy datasets, and in developing DAGs to batch-process millions of records. Experience with general-purpose programming (e.g. Python, Java, Go), dealing with a variety of data structures, algorithms, and serialization formats. Experience with big-data technologies (e.g. Spark, Kafka, Hive). Advanced ability to build reports and dashboards with BI tools (such as Looker and Tableau). Proficiency with Git (or similar version control) and CI/CD best practices. Experience managing workflows using Agile practices. Ability to solve ambiguous problems independently and to manage multiple projects and time constraints simultaneously. Care for the quality of the input data and how the processed data is ultimately interpreted and used. Strong written and verbal communication skills, and strong organizational skills with the ability to act independently and responsibly. Self-starter, comfortable initiating projects from design to execution with minimal supervision, and able to balance multiple (and sometimes competing) priorities in a fast-paced, complex business environment while consistently meeting deadlines. Team player and relationship builder. Experience with OLAP databases such as Snowflake or Redshift. Experience with dbt or similar transformation and semantic-layer frameworks. Experience with digital products, streaming services, or subscription products. Experience with cloud data platforms such as Databricks. Some Important Details.