Lead, Analytics Engineer (BI)

About us

WeWork India is the countrys leading flexible workspace provider that offers a personalized experience to different types of professionals with varied needs through its innovative and customized offerings. WeWork India serves as a vibrant hub for a multifaceted community bringing together innovators visionaries dreamers and high-achievers. It is a collaborative environment designed for mutual inspiration and the collective pursuit of excellence. To gain further insight into our mission and the people behind it you can explore more about our team and organizational structure here or review our culture code to see our values in action.

WeWork India is run by the Indian real estate conglomerate Embassy Group and commenced operations in 2017. Currently it has 105000 members in 70 locations across eight cities in India - Bengaluru Chennai Mumbai Gurugram Noida Delhi Hyderabad and Pune. Its portfolio comprises small and medium enterprises start-ups large businesses freelancers etc. Our mission is focused on empowering tomorrows world of work through achieving Excellence in Execution.

About the role

As an Analytics Engineer the candidate will play a key role in bridging the gap between our data engineering and analytics teams. The candidate will own the transformation layer of our data stack turning raw data into clean reliable and well-documented datasets that power decision-making across the company. The candidate will work closely with data engineers analysts and business stakeholders to build the semantic layer that makes data usable at scale.

To do well in this role candidates need to have a very fine eye for detail experience as a business data analyst and deep understanding of the popular data analysis tools and databases with the goal of providing the insights required to power our organizations growth. The candidate will work closely with multidisciplinary teams and leadership to collect and analyze the data from various sources. Particularly important to the role is the ability to communicate effectively and to maintain open lines of communication with team members and stakeholders.

Roles and responsibilities

• Architect construct and oversee data models within our Snowflake warehouse utilizing dbt.

• Take ownership of the transformation layer converting raw source data into sets ready for advanced analytics.

• Collaborate with analysts and business stakeholders to identify requirements and develop scalable data models.

• Execute data quality assessments monitoring and documentation to maintain a high level of trust in our data.

• Establish and manage essential business metrics within a unified semantic layer.

• Enhance query efficiency and manage warehouse expenditure through optimization.

• Define and promote best practices for version control CI/CD and peer reviews for all analytics code.

• Provide mentorship to analysts regarding SQL best practices and core dimensional modeling principles.

• Play a contributing role in the refinement of our overarching data strategy and architectural framework.

Experience and qualifications

• Over 5 to 9 years of professional experience in analytics engineering data engineering or a comparable technical role.

• Advanced proficiency in SQL with expertise in intricate transformations window functions and performance tuning.

• Direct experience using dbt or similar tools alongside orchestration frameworks like Airflow.

• Solid grasp of data modeling methodologies particularly with Snowflake schemas.

• Familiarity with cloud-based data warehouses such as Snowflake BigQuery Redshift or Databricks.

• Skilled in Git and modern engineering workflows including version control code review and CI/CD.

• Working knowledge of Python for developing scripts and automating processes.

• Experience with BI platforms where knowledge of Tableau is considered a distinct advantage.

• Previous exposure to product analytics platforms like Amplitude Mixpanel or Snowplow is preferred.

• Excellent communication skills with the ability to bridge the gap between business queries and data-driven solutions.