7 months ago
Remote, India or Mumbai, IndiaEntry Level / Mid Level
Responsibilities
- Design, build, and maintain ETL/ELT pipelines ingesting APIs, databases, Kafka/Pub-Sub event streams, and flat files into a central data warehouse.
- Implement incremental and idempotent loading, retry logic, dead-letter queues, SLA-based alerting, backfills, and zero-data-loss practices.
- Own pipeline observability through freshness checks, row-count validation, schema-drift detection, anomaly alerts, and data quality testing.
- Design dimensional and transactional data models, including star and snowflake schemas, slowly changing dimensions, bridge tables, and fact tables.
- Optimize warehouse performance and costs through partitioning, clustering, materialized views, and appropriate table-modeling strategies.
- Write modular, tested Python and SQL; use Git, peer reviews, dbt or equivalent transformation tooling, Docker, and automated deployment pipelines.
- Build Streamlit dashboards, BI semantic models, KPI definitions, and pre-aggregated marts for self-service analytics.
- Use Plotly, Matplotlib, or Altair to create visual narratives and present findings to technical and non-technical audiences.
- Collaborate with product managers, analysts, and data scientists; document lineage, transformation logic, SLAs, and known limitations.
- Contribute to engineering guilds, knowledge-sharing sessions, and post-incident reviews.
Requirements
- Strong SQL skills, including complex queries, performance optimization, and cost-efficient design on BigQuery and Redshift.
- Strong Python scripting experience for ingestion, transformation, validation, APIs, and automation, including hands-on Pandas and SQLAlchemy experience.
- End-to-end ETL/ELT pipeline ownership, including ingestion, transformation, loading, incremental loads, and backfills.
- Ability to design dimensional and transactional data models, including star/snowflake schemas and slowly changing dimensions.
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience.
- 1–3 years of professional experience in data engineering, analytics engineering, or backend engineering with significant data pipeline work.
- Understanding of data warehouse architecture and trade-offs between denormalized and normalized models.
- Familiarity with Git branching strategies, pull requests, code reviews, and agile development practices.
- Experience or familiarity with Airflow, dbt, Docker, CI/CD, GCP, AWS, data warehousing, BI tools, Streamlit, or data visualization.
- Strong data quality mindset, including validating assumptions, adding pipeline assertions, and identifying silent data failures.
Benefits
- MacBook and access to cutting-edge tools and mentorship.
- Medical insurance with COVID Care facilities.
- Mental health support programs.
- Contemporary leave policy beyond sick leave.
- Inclusive, relaxed, creative, and productive work culture with autonomy, open communication, growth opportunities, and work-life balance.
- Monthly and quarterly town hall meetings providing visibility into team and company goals.
Tech Stack
Amazon RedshiftApache AirflowApache KafkaAWSdbtDockerGitGitHub ActionsGitLab CI/CDGoogle BigQueryGoogle Cloud PlatformMatplotlibPandasPythonSQL
Categories
Data Engineering
About WebEngage
WebEngage builds a customer data platform and marketing automation suite for consumer brands, combining unified profiles, journey orchestration, and multichannel messaging across mobile, web, email, and more. It sells its SaaS platform to B2C companies to improve engagement and retention across industries like ecommerce, fintech, and media. Founded in 2011 and headquartered in Mumbai, it is privately held and serves markets across India, the Middle East, and Southeast Asia.