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

2. 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.

3. 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.

4. 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.

5. 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

6. 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

1. Company Description:

Website: www.tatvic.com

2. 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.

3. 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.

4. 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.

5. 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

6. 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

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About Tatvic

Founded :

2009

Type :

Services

Size :

100-1000

Stage :

Bootstrapped

About

Tatvic is an authorized Google Analytics Partner for reselling the 360 Suite license. We are a strategic consulting agency for SMEs and Enterprises for their Data Analytics requirements. We are the only MarTech company in the APAC region that is a Google Marketing Platform, Cloud, and Ads. We are all people with a background in Engineering, IT or Business, whose experience and passions lie and joined together in the world of Web Analytics and Online Marketing. We are Web Analysts, Market Researchers, Web Developers, Technical Analysts, Technical Architectures, and Implementation Specialists, that work all together simultaneously to optimize your online business and to make your conversion rate rocket. We lead the market, but not as it is commonly said by everyone. Our monthly Web Analytics webinars are attended by lots of professionals from all over the world: USA, Brazil, UK, Germany, France, The Netherlands, Italy, to name a few. There must be a reason. Here is the reason: other professionals love the fact that we do not simply keep abreast, we aim at developing new solutions and insights in the Web Analytics field. Similarly, the case studies we provide and the up-to-date articles on our blog keep updating other professionals around the world.

Jenny David

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Aastha Bhadani

Connect

Company social profiles

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Role Overview

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Hands-on experience with Google Cloud Platform (GCP).
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Experience with containerization/orchestration technologies such as Docker/Kubernetes.
Experience with workflow orchestration tools such as Airflow.
Knowledge of cloud-based data architecture and distributed data processing.

Preferred Candidate Profile

Strong analytical and problem-solving abilities.
Good communication and stakeholder management skills.
Ability to work independently as well as in a collaborative team environment.
Strong ownership of data pipelines and production systems.
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Mandatory Skills

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Job Summary

Role Overview

Experience with Google Cloud Platform (GCP) will be an added advantage.

Key Responsibilities

Design, develop, and maintain scalable ETL/ELT data pipelines using Python and SQL.
Develop complex and optimized SQL queries, stored procedures, and data transformations.
Build and maintain reliable data integration workflows across multiple data sources.
Perform data cleansing, validation, transformation, and quality checks.
Analyze data and provide insights to support business and technical requirements.
Implement and maintain CI/CD pipelines for data engineering applications.
Work with DevOps practices and tools to automate deployments, monitoring, and infrastructure processes.
Troubleshoot data pipeline failures, performance issues, and production incidents.
Optimize data processing workflows for performance, scalability, and reliability.
Collaborate with Data Analysts, Data Scientists, Developers, and other stakeholders.
Follow best practices for version control, testing, documentation, and deployment.
Contribute to cloud-based data engineering initiatives, preferably on GCP.

Required Skills

5–7 years of hands-on experience in Data Engineering.
Strong programming skills in Python.
Strong expertise in Advanced SQL and database concepts.
Hands-on experience with ETL/ELT processes and data pipelines.
Good understanding of Data Warehousing and Data Modeling concepts.
Experience with CI/CD practices and tools.
Strong understanding of DevOps principles, automation, and deployment processes.
Strong data analytics and problem-solving skills.
Experience working with large datasets and performance optimization.
Good understanding of Git/version control and software development best practices.

Good to Have

Hands-on experience with Google Cloud Platform (GCP).
Exposure to GCP data services such as BigQuery, Cloud Storage, Dataflow, Composer, or Pub/Sub.
Experience with containerization/orchestration technologies such as Docker/Kubernetes.
Experience with workflow orchestration tools such as Airflow.
Knowledge of cloud-based data architecture and distributed data processing.

Preferred Candidate Profile

Strong analytical and problem-solving abilities.
Good communication and stakeholder management skills.
Ability to work independently as well as in a collaborative team environment.
Strong ownership of data pipelines and production systems.
Candidates who can join at short notice are preferred.

Mandatory Skills

Data Engineer, Python , ETL, GCP, Advanced SQL, Strong Data Analytics skills, CICD, Devops

GCP Data Engineer

at VY SYSTEMS PRIVATE LIMITED

Posted by Banu S

Bengaluru (Bangalore), Hyderabad

5 - 18 yrs

₹4L - ₹35L / yr

Google Cloud Platform (GCP)

GCP

Spark

Ab Initio

Senior Data Engineer – Ab Initio | GCP | Spark | Agentic AI

Location: Bangalore

Experience: 5+ Years

Role: Senior Data Engineer

Work Mode: Bangalore

Job Summary

We are looking for an experienced Senior Data Engineer with strong expertise in Ab Initio, GCP, Apache Spark, and Agentic AI. The ideal candidate will have hands-on experience designing and developing scalable data engineering solutions, building data pipelines, and working with modern cloud and AI technologies.

The candidate should be comfortable working across traditional enterprise data platforms and emerging Generative AI / Agentic AI solutions.

Key Responsibilities

Design, develop, and maintain scalable and high-performance data pipelines using Ab Initio, Spark, and GCP services.
Develop and optimize complex ETL/ELT workflows using Ab Initio.
Build and maintain data processing solutions using Apache Spark / PySpark.
Develop cloud-based data solutions on Google Cloud Platform (GCP).
Work with GCP data services such as BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, or equivalent services.
Perform data integration, transformation, cleansing, and validation.
Optimize data pipelines for performance, scalability, reliability, and cost.
Collaborate with data architects,

Senior Data Engineer – Ab Initio | GCP | Spark | Agentic AI

Location: Bangalore

Experience: 5+ Years

Role: Senior Data Engineer

Work Mode: Bangalore

Job Summary

The candidate should be comfortable working across traditional enterprise data platforms and emerging Generative AI / Agentic AI solutions.

Key Responsibilities

Design, develop, and maintain scalable and high-performance data pipelines using Ab Initio, Spark, and GCP services.
Develop and optimize complex ETL/ELT workflows using Ab Initio.
Build and maintain data processing solutions using Apache Spark / PySpark.
Develop cloud-based data solutions on Google Cloud Platform (GCP).
Work with GCP data services such as BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, or equivalent services.
Perform data integration, transformation, cleansing, and validation.
Optimize data pipelines for performance, scalability, reliability, and cost.
Collaborate with data architects,

GCP Data Engineer

at VY SYSTEMS PRIVATE LIMITED

Posted by Pavithra E

Bengaluru (Bangalore), Hyderabad

9 - 15 yrs

₹8L - ₹24L / yr

GCP

PL/SQL

Python

PySpark

🚨 Hiring: GCP Data Engineer

We are looking for experienced GCP Data Engineers to join our team!

🔹 Experience: 9+ Years

🔹 Relevant Experience: 4+ Years in GCP Data Engineering

🔹 Required Skills: GCP, Oracle PL/SQL, Python, PySpark

🔹 Location: Bangalore / Hyderabad

🔹 Notice Period: Immediate to 10 Days Preferred

Key Skills:

🔹 Strong hands-on experience in GCP Data Engineering

🔹 Good experience with PySpark & Python

🔹 Strong knowledge of Oracle PL/SQL

🔹 Experience in data processing, ETL, and data pipelines

🔹 Good understanding of cloud-based data engineering

📩 Interested candidates can share their updated CV via DM.

#Hiring #GCPDataEngineer #GCP #DataEngineering #PySpark #Python #OraclePLSQL #DataEngineer #BangaloreJobs #HyderabadJobs #ImmediateJoiner #TechJobs #ITJobs #HiringNow

🚨 Hiring: GCP Data Engineer

We are looking for experienced GCP Data Engineers to join our team!

🔹 Experience: 9+ Years

🔹 Relevant Experience: 4+ Years in GCP Data Engineering

🔹 Required Skills: GCP, Oracle PL/SQL, Python, PySpark

🔹 Location: Bangalore / Hyderabad

🔹 Notice Period: Immediate to 10 Days Preferred

Key Skills:

🔹 Strong hands-on experience in GCP Data Engineering

🔹 Good experience with PySpark & Python

🔹 Strong knowledge of Oracle PL/SQL

🔹 Experience in data processing, ETL, and data pipelines

🔹 Good understanding of cloud-based data engineering

📩 Interested candidates can share their updated CV via DM.

#Hiring #GCPDataEngineer #GCP #DataEngineering #PySpark #Python #OraclePLSQL #DataEngineer #BangaloreJobs #HyderabadJobs #ImmediateJoiner #TechJobs #ITJobs #HiringNow

Data Engineer Databricks/Snowflake

at Wissen Technology

4 recruiters

Posted by Robin Silverster

Bengaluru (Bangalore)

7 - 10 yrs

₹15L - ₹40L / yr

Data engineering

Python

PySpark

Data Transformation Tool (DBT)

Apache Airflow

+2 more

About the Role

We are looking for a Senior Data Engineer with strong hands-on expertise in Databricks, Python, PySpark, and SQL to build scalable, high-performance data engineering solutions. You’ll architect and develop large scale, high-performance data pipelines capable of handling massive real-time and batch data volumes across multiple business systems. Databricks is the core enterprise data and processing platform for this role. You will also use Apache Airflow for workflow orchestration and dbt for ELT transformations, and will contribute to designing reliable, secure, and governed data platforms that enable analytics, reporting, and AI-driven use cases.

Key Responsibilities

Design and implement large-scale data pipelines using Python/PySpark, Databricks, and Microsoft Fabric.
Develop and optimize data processing workloads in Databricks using PySpark and Spark SQL, with a strong focus on scalability, reliability, performance, and maintainability.
Develop and maintain dbt models including layered architecture, incremental models, snapshots, macros, testing, and documentation.
Design, develop, and maintain Apache Airflow DAGs for orchestrating reliable, scalable, and observable data pipelines.
Design and implement data quality, observability, and governance frameworks, including automated testing, monitoring, lineage, access control, and data privacy standards.
Partner with analytics, product, and business stakeholders to turn requirements into trustworthy datasets, and raise the engineering bar through design discussions, code reviews, and mentoring junior engineers.

Required Skills

Strong expertise in Python for developing scalable, modular, and production-ready data engineering applications.
Strong expertise in PySpark, including DataFrame API, Spark SQL, Structured Streaming, partitioning strategies, joins, caching, handling data skew, and Spark performance optimization.
Strong hands-on experience with Databricks for data ingestion, transformation, processing, and optimization, including Delta Lake, Unity Catalog, Databricks Workflows, notebooks, jobs, and Databricks-native data engineering capabilities.
Strong experience in Databricks/Spark performance tuning, including query and job optimization, partitioning, file sizing, caching, join optimization, handling data skew, and efficient use of compute resources.
Hands-on experience with Delta Lake, including transactional data processing, schema management, incremental data processing, and reliable batch and streaming data pipelines.
Hands-on experience in developing dbt projects using layered architecture, incremental models, snapshots, macros/Jinja, testing, documentation, and deployment best practices.
Expertise in advanced SQL and data modelling — dimensional modeling, slowly changing dimensions, schema evolution, and query optimization.
Hands-on experience in developing and managing Apache Airflow DAGs, scheduling workflows, dependency management, retries, backfills, and operational monitoring.
Hands-on experience with at least one major cloud platform (AWS, Azure or GCP).
Strong problem-solving skills and the ability to work independently with business and analytics stakeholders.

Nice to Have

Hands-on exposure to Microsoft Fabric for data integration and analytics.
Experience using AI coding assistants (e.g. Claude Code, GitHub Copilot) as part of a development workflow.
Familiarity with modern DevOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and containerization (Docker/Kubernetes).
Domain expertise in financial services.

About the Role

Key Responsibilities

Design and implement large-scale data pipelines using Python/PySpark, Databricks, and Microsoft Fabric.
Develop and optimize data processing workloads in Databricks using PySpark and Spark SQL, with a strong focus on scalability, reliability, performance, and maintainability.
Develop and maintain dbt models including layered architecture, incremental models, snapshots, macros, testing, and documentation.
Design, develop, and maintain Apache Airflow DAGs for orchestrating reliable, scalable, and observable data pipelines.
Design and implement data quality, observability, and governance frameworks, including automated testing, monitoring, lineage, access control, and data privacy standards.
Partner with analytics, product, and business stakeholders to turn requirements into trustworthy datasets, and raise the engineering bar through design discussions, code reviews, and mentoring junior engineers.

Required Skills

Strong expertise in Python for developing scalable, modular, and production-ready data engineering applications.
Strong expertise in PySpark, including DataFrame API, Spark SQL, Structured Streaming, partitioning strategies, joins, caching, handling data skew, and Spark performance optimization.
Strong hands-on experience with Databricks for data ingestion, transformation, processing, and optimization, including Delta Lake, Unity Catalog, Databricks Workflows, notebooks, jobs, and Databricks-native data engineering capabilities.
Strong experience in Databricks/Spark performance tuning, including query and job optimization, partitioning, file sizing, caching, join optimization, handling data skew, and efficient use of compute resources.
Hands-on experience with Delta Lake, including transactional data processing, schema management, incremental data processing, and reliable batch and streaming data pipelines.
Hands-on experience in developing dbt projects using layered architecture, incremental models, snapshots, macros/Jinja, testing, documentation, and deployment best practices.
Expertise in advanced SQL and data modelling — dimensional modeling, slowly changing dimensions, schema evolution, and query optimization.
Hands-on experience in developing and managing Apache Airflow DAGs, scheduling workflows, dependency management, retries, backfills, and operational monitoring.
Hands-on experience with at least one major cloud platform (AWS, Azure or GCP).
Strong problem-solving skills and the ability to work independently with business and analytics stakeholders.

Nice to Have

Hands-on exposure to Microsoft Fabric for data integration and analytics.
Experience using AI coding assistants (e.g. Claude Code, GitHub Copilot) as part of a development workflow.
Familiarity with modern DevOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and containerization (Docker/Kubernetes).
Domain expertise in financial services.

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