About the role

OpenGov is looking for a high-ownership, solution-oriented Data Engineer to join our rapidly growing Data Platform team. You'll work hands-on with dbt to model and transform data that powers decision-making across the organization and you'll take end-to-end ownership of how that work gets shipped: version-controlled, tested, and deployed through CI/CD rather than simply handed off. You think in terms of data models, business domains, and consumption patterns, but you're equally comfortable working close to the infrastructure your data pipelines run on. You view data and infrastructure similar to software that is maintained, not just stateless scripts that are deployed and forgotten about. You partner closely with business analytics, product, and AI teams to understand their needs and deliver data solutions that are well-structured, well-documented, and production-ready.

What You'll Own

- Stakeholder Engagement & Requirement Gathering: Partner with business stakeholders across Analytics, Operations, GTM, and G&A; to understand problem statements and translate them into clear data requirements.
- Data Modeling & Transformation: Design and build dimensional and analytical data models in Snowflake using dbt (Cloud/Core) that serve as the single source of truth for business domains. Develop, test, and maintain dbt models following best practices — modular layering (staging, intermediate, marts), documentation, and data tests. Optimize SQL queries and dbt models for performance using Snowflake features like clustering, materialization strategies, and query profiling.
- Pipeline Scheduling & Deployment: Own scheduling and orchestration of dbt jobs, ensuring reliability, observability,



and SLA adherence. Own CI/CD workflows for dbt and pipeline deployments using GitHub Actions — automated testing, linting, and promotion through environments. Work with the Data Platform team on AWS resource provisioning (via Terraform) needed to support ingestion and pipeline infrastructure.
- Data Quality & Analysis: Implement data quality checks and freshness tests within dbt to proactively catch issues before they reach consumers. Perform exploratory data analysis to validate source data, understand distributions, and surface anomalies during scoping and build phases.

What You Bring

- Bachelor's degree in Computer Science, Mathematics, Engineering, Statistics, or a related field.
- 4–6 years of experience in data engineering, backend engineering, or a similar role.
- Data warehouse & dbt Proficiency: Hands-on experience building and maintaining dbt projects in a data warehouse (preferably Snowflake).
- DevOps Experience: Experience building or maintaining CI/CD pipelines (GitHub Actions preferred) for automated testing and deployment of pipelines.
- Data Modeling Skills: Solid understanding of dimensional modeling (star schema, preventing schema drift, etc. ) and the ability to design models that balance clarity, performance, and flexibility.
- Cloud Familiarity:



Hands-on experience with core AWS services (e.g., S3, Lambda, IAM).
- SQL Mastery: Advanced SQL — complex transformations, window functions, etc.
- Python: Proficiency in Python for data manipulation, pipeline scripting, and automation.
- Data Analysis: Comfort exploring raw data, identifying quality issues, communicating findings clearly.
- Software Engineering Practices: You treat data code like software — version-controlled, peer-reviewed, tested, documented.
- Robust communication and collaboration skills across technical and non-technical audiences.
- Experience using AI-assisted development tools (e.g., Claude Code, Cursor, Codex) to accelerate engineering workflows.

Nice to Have

- Some working exposure to Terraform or other IaaC for provisioning/managing cloud resources.
- Comfort in discovery sessions with business stakeholders, translating their analytical problems into well-structured data models.
- Exposure to a workflow orchestrator (e.g., Airflow, Dagster, Prefect) — not required day-to-day, but a plus.
- Familiarity with dbt Mesh concepts (cross-project references, data contracts) or multi-project dbt architectures.
- Exposure to data ingestion tools such as Fivetran, Airbyte, or AWS Glue.
- Experience working with business application data (e.g., Salesforce, Workday, NetSuite) and its quirks.
- Familiarity with BI tooling (e.g., Tableau, Looker) and how data models serve visualization layers.
- Exposure to data cataloging/discovery tools (e.g., DataHub).
- Any exposure to containerized deployments (Docker, SPCS) — not required, but a plus if present.

📌 Data Engineer (Pune)
🏢 OpenGov
📍 Pune