Analytics Engineer
Analytics Engineer at CommVersion, a managed live-chat and AI-assisted conversation product for websites. Day to day you own dbt models end to end in Redshift, build dlt ingestion and custom Python API loaders, orchestrate with Airflow 3, and power Metabase/Power BI dashboards for client and revenue reporting.
- Compensation
- ₹600K–₹800K / yr
- Location
- Mumbai Suburban
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 2+ years
- Category
- Data analytics
- Posted
Skills
Must have
- 2+ years in an analytics engineering, data engineering or BI engineering role (internships/academic projects don't count)
- Strong SQL: window functions, CTEs, multi-table joins, aggregation at the right grain
- Working Python: requests/pandas, clean functions, error handling and logging
- Data modelling fundamentals: facts vs dimensions, grain, keys
- Branch-and-pull-request workflow with code review
- Clear written and spoken English for non-technical stakeholders
Nice to have
- Hands-on production dbt: ref/source, materializations, incremental models, tests, macros, Jinja
- Any orchestrator: Airflow, Dagster, Prefect, Mage
- Any EL tooling: dlt, Fivetran, Airbyte, Meltano
- Dashboards built in any BI tool (Metabase, Power BI, Tableau, Looker, Superset) with real stakeholders
- Redshift or another columnar MPP warehouse (Snowflake, BigQuery, Databricks)
- GA4 / BigQuery export, Google Ads or other marketing data sources
- Working with LLM or ML service outputs as a data source (scoring, classification, NER)
Full description
Analytics Engineer
Full-time
CommVersion runs managed live-chat and AI-assisted conversation products for
websites across hundreds of client accounts. Every chat, greeting, lead and page view
lands in our warehouse, and the data team turns that into the client dashboards,
executive reporting and revenue models the business runs on.
We are looking for an Analytics Engineer to own models in our dbt codebase end to end from raw loaded tables through to the marts behind our Metabase and Power
BI dashboards. This is a hands-on build role, not a reporting-request queue. You will
write SQL and Python that runs in production every hour.
This suits someone roughly 2 years in who already writes real dbt models and solid
SQL, and wants to go deeper on data modelling, orchestration and warehouse
performance with a small, senior team around them.
Our Stack:
You will work in all of this. We are not asking you to already know all of it we are asking you to
be strong in SQL, dbt and Python, and to pick up the rest fast.
What You'll Do:
Build and own dbt models across our staging, intermediate, fact/dim and reporting layers including incremental models with merge strategies, SCD-style history tables and change-log driven refresh logic.
Model the domain properly clients, agents, products, chats, leads, traffic and revenue with dimensional thinking, sane grain and keys that survive contact with real data.
Write dbt tests and data-quality checks (generic, singular and custom) so that a bad
upstream load fails loudly instead of quietly reaching a client dashboard.
Build ingestion with dlt new sources, incremental/persistent vs non-persistent load
patterns, schema evolution, and migrating our remaining legacy connectors over.
Write Python where the tooling stops pull from third-party APIs that dlt doesn't cover,
handle pagination, rate limits and retries, land the data in Redshift, and call our existing AI services for enrichment.
Orchestrate in Airflow 3 author and maintain DAGs, wire dependencies between load,
transform and report stages, and keep run times and retries under control.
Build dashboards in Metabase and Power BI on top of your own models, and work directly with commercial and operations stakeholders on what they actually need.
Care about performance distribution and sort keys, join strategy, incremental
windows, and why the daily run got 40 minutes slower this week.