WHAT YOU'LL DO

- Design, build, and maintain robust ETL/ELT pipelines feeding a Snowflake-based data platform
- Build and manage integrations using SnapLogic to connect source systems, APIs, and downstream consumers
- Develop and maintain data models and transformations in dbt, including tests, documentation, and CI/CD-based deployment
- Design dimensional and/or medallion-style (Bronze/Silver/Gold) data architectures that balance performance, cost, and usability
- Use AI-assisted tools to accelerate development generating boilerplate code, drafting SQL/dbt models, writing documentation, debugging pipeline failures, and summarising data quality issues
- Partner with data quality, governance, and analytics teams to ensure data is well-modelled, well-documented, and trustworthy
- Optimise Snowflake warehouse performance and cost (query tuning, clustering, resource monitors)
- Write clean, tested, version-controlled code and contribute to CI/CD pipelines
- Mentor junior engineers, including on how to use AI tools responsibly and effectively (e.g., reviewing AI-generated code, not blindly trusting output)
- Contribute to internal standards for prompt patterns, reusable AI workflows, or tooling that make the whole team faster

CORE SKILLS

- Snowflake strong hands-on experience with data modelling, performance tuning, security/access, and cost management
- SnapLogic building and maintaining integration pipelines and connecting heterogeneous source systems
- dbt writing modular, tested transformations; managing dependencies, macros, and documentation




- Data Modelling — dimensional modelling, medallion/layered architectures, normalisation vs. denormalisation trade-offs
- Solid SQL and at least one scripting language (Python preferred)
- Familiarity with orchestration tools (Airflow, ADF, or similar)
- Working knowledge of git-based CI/CD workflows

AI-AUGMENTED WORKING STYLE (WHAT WE'RE LOOKING FOR)

- Regularly uses AI coding assistants (Copilot, Claude Code, Cursor, ChatGPT, etc.) as part of the daily workflow — not just for one-off snippets
- Comfortable prompting AI tools for tasks like generating dbt models, writing test cases, summarising data quality issues, or drafting documentation
- Applies good judgement about when AI output needs review vs. can be trusted — treats AI as a fast first draft, not a final answer
- Curious about applying AI to structural problems: pipeline debugging, anomaly detection, metadata generation, code review support
- Comfortable working in an environment where AI-usage practices are still evolving, and contributes ideas to shape them

NICE TO HAVE

- Experience with data quality tooling (SODA,Collibra, or similar)
- Exposure to cloud platforms (Azure, AWS, or GCP)
- Experience in a regulated or enterprise-scale data environment
- Prior experience mentoring or leading a small pod of engineers

EXPERIENCE

- 8+ years in data engineering, with at least 4+ years focused on Snowflake and modern ELT tooling (dbt)
- Track record of delivering production-grade pipelines at scale

Interested candidate can send resume [email protected]

📌 Senior Data Engineer (Hyderabad)
🏢 TechStar Group
📍 Hyderabad