Data Engineer

Responsibilities:
Partner directly with the Noida analyst team to understand their workflows, surface bottlenecks, and build the data products that make them faster and more accurate.
Define the canonical metrics for coverage, speed, and accuracy across the business. Today, these terms get used loosely. You'll fix that.
Build and maintain dbt models in our Snowflake warehouse that turn operational data into trustworthy, queryable assets.
Migrate fragmented reporting (spreadsheets, legacy PowerBI dashboards) into Metabase as the canonical source of truth for operational and pipeline health.
Measure pipeline efficiency end-to-end. From document acquisition through extraction, analyst review, and delivery. Surface where time and quality are leaking.
Partner with the existing data hire to share the load across foundations, product analytics, and BizOps as the team grows.
Be the data team's voice in operational planning conversations.
Push back on decisions made without data, and make data easier to get to so that it stops happening.
Requirements:
4+ years in analytics engineering, data engineering, or a closely adjacent role.
Strong dbt and SQL skills. You've shipped models that other people depend on, not just one-off queries.
Hands-on with a modern data warehouse (Snowflake, Redshift, or BigQuery) and orchestration (Airflow, Dagster, or equivalent).
Comfortable with a BI tool (Metabase, Looker, or Tableau) for building dashboards that operational stakeholders actually use.
Experience working closely with an operations, content, or analyst team. You understand that the goal isn't a beautiful dashboard; it's a faster, more accurate team.
Strong instinct for metric design. You can take a fuzzy concept like "accuracy" and propose a definition that's measurable, defensible, and actually useful.
Clear communicator. You can write the doc that explains what a metric means and why, and hold a room when stakeholders disagree on definitions.
Bonus Points for:
Experience replacing legacy reporting (PowerBI, spreadsheets, ad-hoc SQL) with consolidated, governed dashboards.
Background in fintech, financial data, or other domains where data quality and audit trails matter.
Experience measuring human-in-the-loop or annotation pipelines, where throughput, accuracy, and rework rates all matter.
Familiarity with Python for data work (pandas, lightweight ETL, ad-hoc analysis).
Comfort working across time zones with distributed teams.

Experience
5-9 yrs