Lead - Data Engineering

1. Company Description:

Tatvic, is a marketing analytics company focusing on generating insights from data using long association with Google & its infrastructure. We breed,recognize and reward performance. As a company we are growing very fast & we are under transformation. To enable this transformation we need future leaders with eyesight which balances execution & strategic understanding.

Website: www.tatvic.com

1. Role Details
   Role: Lead Data Engineer
   Band: L4
   Department: Data Engineering
   Reporting to: L5 / Head of Delivery
   Location: Ahmedabad
   Job Type: Full Time
   Experience: 6-8 years of relevant experience
   Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related technical field.

2. Mission & Role Overview
   Mission: To create high-level organizational impact by leading a team to design and deliver scalable, high-performance cloud-native data solutions. The Lead Data Engineer (L4) is responsible for ensuring technical excellence—specifically in BigQuery optimization and secure GCP/AWS architectures—while driving the transition to Agentic AI workflows and creating value through the development of L1-L3 engineers.

Role Overview: This is a pivotal leadership role on the Management Track, responsible for end-to-end data outcomes for the function. The Lead Engineer ensures the quality of engineering execution, manages team utilization and resource allocation, and serves as the primary technical representative in cross-functional forums with Marketing, Strategy, and Product teams.

1. Key Responsibilities
   Lead the design, development, and deployment of scalable and efficient cloud-based data pipelines and architectures.
   Build reusable data solutions and software components that serve multiple customers and use cases.
   Architect cloud environments using GCP, ensuring best practices in IAM, Security, and Networking.
   Conduct Exploratory Data Analysis (EDA) on clickstream and behavioral datasets to derive insights.
   Manage end-to-end ETL processes, including data extraction, transformation, and loading using Python, SQL, and Airflow.
   Design and build production-grade software (APIs, services, internal tools) with sound engineering practices — version control, testing, CI/CD, and code review.
   Extend data platforms with AI capabilities — integrating LLMs, RAG pipelines, and agentic workflows where they add clear business value.
   Perform Root Cause Analysis (RCA) and resolve data quality, pipeline, or application issues.
   Collaborate cross-functionally with Marketing, Insights, Product, and Engineering teams to align data initiatives with business goals.
   Document architectures, design decisions, and operations clearly and consistently.
   Coach and mentor team members, ensuring technical excellence and continuous learning.
   Participate in recruitment and knowledge-sharing through interviews, blogs, and webinars.

2. Must-Have & Good-to-Have Skills
   Must-Have Skills:
   Strong understanding of clickstream data and data pipeline architecture
   Hands-on experience with cloud platforms (GCP)
   Expertise in SQL, Python, and Airflow
   Solid software engineering foundation — API design, testing, version control, and deployment practices
   Proven ability to perform exploratory data analysis and data validation
   Knowledge of cloud security, IAM, and networking best practices
   Working knowledge of building or integrating AI/LLM-based components into existing systems
   Excellent problem-solving, stakeholder management, and communication skills

Good-to-Have Skills:
Certifications such as: Google Cloud Professional Data Engineer / Cloud Architect
AWS Certified Data Analytics or Azure Data Engineer Associate
Google Analytics 4 (GAIQ)
Experience in marketing attribution, commerce data, or analytics ecosystems
Exposure to cost optimization and cloud resource management
Experience with agent frameworks (LangChain, ADK, MCP) or LLM APIs
Experience building automation frameworks or internal data tools

1. Not-a-Fit Criteria Individual Contributor Focus: Operates as the team's best individual contributor instead of building team capability or empowering L3s. Information Hoarding: Keeps architectural patterns or tribal knowledge trapped in their head rather than creating reusable playbooks and documentation. Avoidance of Conflict: Avoids difficult performance conversations with underperforming team members, allowing capability gaps to persist. Activity Over Outcomes: Measures success by "tasks completed" rather than pipeline stability, data accuracy, or business impact. Functional Siloing: Fails to build partnerships with Marketing or Product teams, leading to misaligned data delivery.

Our Culture & Perks - Where Passion Meets Purpose:
Competitive CTC: Based on experience and skillset
Medical Insurance for you and your family
Return-to-Work Program for New Moms — because career breaks shouldn’t break momentum
1 Work From Home Day/Month — flexibility when life calls
2 Birthday Leaves/Year — one for you, one for someone you love
Monthly Engagement Activities — from game nights to learning sessions
Inclusive Culture & Equal Opportunity Employer — diversity drives our innovation
We Think Like Engineers & Athletes: we build, we iterate, we scale, we win — together
5-day workweek — because balance drives brilliance