Senior Data Engineer

Prudent Technologies and Consulting, Inc.; Hyderabad; On-site; INR 2,800,000 - 4,000,000; Full time

Job summary

Prudent Technologies and Consulting, Inc. is seeking a seasoned Data Engineer to design, build, and maintain robust ETL/ELT pipelines on a Snowflake-based platform. You will connect diverse source systems via SnapLogic and develop modular dbt models with tests and CI/CD deployments.

You will craft Bronze/Silver/Gold architectures, optimise performance and cost, and mentor junior engineers. AI-assisted coding and strong collaboration with governance teams are expected.

Qualifications

• 8+ years in data engineering with Snowflake and modern ELT tooling (dbt).
• Experience delivering production-grade pipelines at scale.
• Strong SQL and Python skills; able to design scalable data models.

Responsibilities

• Design, build, and maintain robust ETL/ELT pipelines on Snowflake.
• Build and manage integrations using SnapLogic to connect source systems and APIs.
• Develop and maintain dbt models with tests and CI/CD deployments.
• Design Bronze/Silver/Gold data architectures balancing performance and cost.
• Use AI-assisted tools to accelerate development and generate boilerplate code.
• Partner with data quality, governance, and analytics teams to ensure trustworthy data.
• Optimise Snowflake performance and cost; tune queries and manage resources.
• Write clean, tested, version-controlled code and contribute to CI/CD pipelines.
• Mentor junior engineers and promote responsible AI tool usage.
• Contribute to internal standards for prompt patterns and reusable AI workflows.

Skills

Snowflake data modelling

SnapLogic

dbt

Data modelling

SQL

Python

Orchestration (Airflow/ADF)

Git CI/CD workflows

AI-assisted development

Mentoring

Tools

Snowflake

SnapLogic

dbt

Airflow

ADF

Git

Job description

• 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
• Strong 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