Back to jobs

Technology and Innovation - Data Engineering Manager

India

Pay
Salary not listed in the saved posting
Work setup
Unconfirmed
Employment
Unconfirmed
Apply at Riveron

What you’ll work on

Full posting
  • Write and maintain shared libraries, frameworks, and reusable components that accelerate the team's delivery and enforce engineering standards.

  • Partner with analytics, data science, and business stakeholders to translate data requirements into engineered solutions and personally prototype key components.

  • Optimize platform performance and manage costs across Snowflake compute, Databricks clusters, and Fabric capacities through hands-on tuning and configuration.

From the employer’s posting
Own critical and complex implementation work — the toughest pipeline builds, the most challenging performance problems, and the architectural spikes that set direction for the team. Write and maintain shared libraries, frameworks, and reusable components that accelerate the team's delivery and enforce engineering standards. Conduct deep, line-level code reviews focused on correctness, performance, and maintainability — not just approval stamps.
Conduct deep, line-level code reviews focused on correctness, performance, and maintainability — not just approval stamps. Partner with analytics, data science, and business stakeholders to translate data requirements into engineered solutions and personally prototype key components. Establish and enforce best practices for data modeling, pipeline reliability, observability, testing, and documentation — by building the reference implementations yourself.
Establish and enforce best practices for data modeling, pipeline reliability, observability, testing, and documentation — by building the reference implementations yourself. Optimize platform performance and manage costs across Snowflake compute, Databricks clusters, and Fabric capacities through hands-on tuning and configuration. Implement data governance and security controls across engineering initiatives, ensuring compliance, data quality, and lineage traceability at the code and platform level.

What you’ll bring

All qualifications

Core experience

  • Deep proficiency in SQL and Python — you can write complex transformations, optimize query performance, debug pipeline failures, and build automation frameworks yourself.
  • Proven experience building and shipping ETL/ELT frameworks using Azure Data Factory, dbt, Spark, and platform-native orchestration tools.
  • Proficient with Git-based version control, CI/CD pipelines for data workloads, and Infrastructure as Code — you commit code, open pull requests, and maintain deployment pipelines alongside your team.

Preferred experience

  • Hands-on experience building medallion-architecture lakehouses on Databricks or Microsoft Fabric, and designing and tuning performant Snowflake warehouse structures.
  • Experience implementing CI/CD for data pipelines using Azure DevOps or GitHub Actions, including writing the pipeline definitions and deployment scripts yourself.
  • Familiarity with dbt for transformation orchestration, with personal experience writing and maintaining dbt models and tests.
  • Experience with data mesh or domain-oriented data platform strategies is a strong plus.
Qualification wording
Deep proficiency in SQL and Python — you can write complex transformations, optimize query performance, debug pipeline failures, and build automation frameworks yourself.
Proven experience building and shipping ETL/ELT frameworks using Azure Data Factory, dbt, Spark, and platform-native orchestration tools.
Proficient with Git-based version control, CI/CD pipelines for data workloads, and Infrastructure as Code — you commit code, open pull requests, and maintain deployment pipelines alongside your team.
Hands-on experience building medallion-architecture lakehouses on Databricks or Microsoft Fabric, and designing and tuning performant Snowflake warehouse structures.
Experience implementing CI/CD for data pipelines using Azure DevOps or GitHub Actions, including writing the pipeline definitions and deployment scripts yourself.
Familiarity with dbt for transformation orchestration, with personal experience writing and maintaining dbt models and tests.
Experience with data mesh or domain-oriented data platform strategies is a strong plus.

Tools in this posting

  • Python
  • SQL
  • Azure
  • Databricks
  • dbt
  • Kafka
  • Snowflake
  • Spark
Source — Tool mentions in context
- Strong data architecture background spanning lakehouse design, medallion architecture, data vault, dimensional modeling, and enterprise data platform design — grounded in systems you've actually built, not just designed on paper. - Deep proficiency in SQL and Python — you can write complex transformations, optimize query performance, debug pipeline failures, and build automation frameworks yourself. - Proven experience building and shipping ETL/ELT frameworks using Azure Data Factory, dbt, Spark, and platform-native orchestration tools.
- Proven experience building and shipping ETL/ELT frameworks using Azure Data Factory, dbt, Spark, and platform-native orchestration tools. - Solid working knowledge of Azure cloud data services (Synapse Analytics, Data Lake Storage, Azure SQL Database) and comfort operating in multi-cloud environments. - Well-versed in data governance, data quality frameworks, cataloging, lineage, and security best practices — with experience implementing these controls, not just defining policies.
- Deep proficiency in SQL and Python — you can write complex transformations, optimize query performance, debug pipeline failures, and build automation frameworks yourself. - Proven experience building and shipping ETL/ELT frameworks using Azure Data Factory, dbt, Spark, and platform-native orchestration tools. - Solid working knowledge of Azure cloud data services (Synapse Analytics, Data Lake Storage, Azure SQL Database) and comfort operating in multi-cloud environments.
- Hands-on experience building medallion-architecture lakehouses on Databricks or Microsoft Fabric, and designing and tuning performant Snowflake warehouse structures. - Experience implementing CI/CD for data pipelines using Azure DevOps or GitHub Actions, including writing the pipeline definitions and deployment scripts yourself. - Familiarity with dbt for transformation orchestration, with personal experience writing and maintaining dbt models and tests.
Data Engineering Manager Riveron is seeking a hands-on Data Engineering Manager to be part of our data engineering team in India. This is not a pure people-management role — you will be writing code, building pipelines, and architecting data platforms daily alongside your team. We're looking for someone who leads from the codebase, not just from a conference room. If you bring deep implementation experience across Microsoft Fabric, Snowflake, and Databricks and can pair strong architectural thinking with real delivery output, we'd love to hear from you. Who You Are:
- You write production-quality code daily. This role requires active coding and pipeline development — not just code reviews and architectural diagrams. - Hands-on, implementation-level expertise with Microsoft Fabric, Snowflake, and Databricks is non-negotiable. You've personally built, deployed, and tuned production workloads on these platforms. - Strong data architecture background spanning lakehouse design, medallion architecture, data vault, dimensional modeling, and enterprise data platform design — grounded in systems you've actually built, not just designed on paper.
- Lead, mentor, and grow a team of data engineers while remaining the team's strongest technical contributor and setting the standard through your own code and architectural decisions. - Design and personally implement scalable data platforms on Microsoft Fabric, Snowflake, and Databricks — selecting the right platform for each use case and building the solution end to end. - Architect and build lakehouse and data warehouse solutions, defining and coding ingestion patterns, transformation layers, storage strategies, and consumption models.
- Establish and enforce best practices for data modeling, pipeline reliability, observability, testing, and documentation — by building the reference implementations yourself. - Optimize platform performance and manage costs across Snowflake compute, Databricks clusters, and Fabric capacities through hands-on tuning and configuration. - Implement data governance and security controls across engineering initiatives, ensuring compliance, data quality, and lineage traceability at the code and platform level.
- A career path that shows sustained, hands-on implementation even as responsibilities grew — architect-turned-manager, not analyst-turned-manager. - Hands-on experience building medallion-architecture lakehouses on Databricks or Microsoft Fabric, and designing and tuning performant Snowflake warehouse structures. - Experience implementing CI/CD for data pipelines using Azure DevOps or GitHub Actions, including writing the pipeline definitions and deployment scripts yourself.
- Experience implementing CI/CD for data pipelines using Azure DevOps or GitHub Actions, including writing the pipeline definitions and deployment scripts yourself. - Familiarity with dbt for transformation orchestration, with personal experience writing and maintaining dbt models and tests. - Exposure to real-time or near-real-time data ingestion patterns (Kafka, Event Hubs, Spark Structured Streaming) through direct implementation.
- Familiarity with dbt for transformation orchestration, with personal experience writing and maintaining dbt models and tests. - Exposure to real-time or near-real-time data ingestion patterns (Kafka, Event Hubs, Spark Structured Streaming) through direct implementation. - Experience with data mesh or domain-oriented data platform strategies is a strong plus.

About Riveron

At Riveron, we partner with clients—from global multinationals to high-growth private entities—to solve complex finance challenges, guided by our DELTA values: Drive, Excellence, Leadership, Teamwork, and Accountability.

In the employer’s words · Read in context

Job description

View original posting ↗

Data Engineering Manager

Riveron is seeking a hands-on Data Engineering Manager to be part of our data engineering team in India. This is not a pure people-management role — you will be writing code, building pipelines, and architecting data platforms daily alongside your team. We're looking for someone who leads from the codebase, not just from a conference room. If you bring deep implementation experience across Microsoft Fabric, Snowflake, and Databricks and can pair strong architectural thinking with real delivery output, we'd love to hear from you.

Who You Are:

  • A data engineering professional with 7-9 years of experience, including 1-3 years balancing technical leadership with active, hands-on implementation — you've managed teams without stepping away from the keyboard.

  • You write production-quality code daily. This role requires active coding and pipeline development — not just code reviews and architectural diagrams.

  • Hands-on, implementation-level expertise with Microsoft Fabric, Snowflake, and Databricks is non-negotiable. You've personally built, deployed, and tuned production workloads on these platforms.

  • Strong data architecture background spanning lakehouse design, medallion architecture, data vault, dimensional modeling, and enterprise data platform design — grounded in systems you've actually built, not just designed on paper.

  • Deep proficiency in SQL and Python — you can write complex transformations, optimize query performance, debug pipeline failures, and build automation frameworks yourself.

  • Proven experience building and shipping ETL/ELT frameworks using Azure Data Factory, dbt, Spark, and platform-native orchestration tools.

  • Solid working knowledge of Azure cloud data services (Synapse Analytics, Data Lake Storage, Azure SQL Database) and comfort operating in multi-cloud environments.

  • Well-versed in data governance, data quality frameworks, cataloging, lineage, and security best practices — with experience implementing these controls, not just defining policies.

  • Proficient with Git-based version control, CI/CD pipelines for data workloads, and Infrastructure as Code — you commit code, open pull requests, and maintain deployment pipelines alongside your team.

  • Comfortable working in Agile delivery environments and translating business requirements into scalable technical designs.

What You'll Do:

  • Spend a significant portion of your time writing code — building data pipelines, developing transformation logic, configuring platform services, and shipping production-ready solutions.

  • Lead, mentor, and grow a team of data engineers while remaining the team's strongest technical contributor and setting the standard through your own code and architectural decisions.

  • Design and personally implement scalable data platforms on Microsoft Fabric, Snowflake, and Databricks — selecting the right platform for each use case and building the solution end to end.

  • Architect and build lakehouse and data warehouse solutions, defining and coding ingestion patterns, transformation layers, storage strategies, and consumption models.

  • Own critical and complex implementation work — the toughest pipeline builds, the most challenging performance problems, and the architectural spikes that set direction for the team.

  • Write and maintain shared libraries, frameworks, and reusable components that accelerate the team's delivery and enforce engineering standards.

  • Conduct deep, line-level code reviews focused on correctness, performance, and maintainability — not just approval stamps.

  • Partner with analytics, data science, and business stakeholders to translate data requirements into engineered solutions and personally prototype key components.

  • Establish and enforce best practices for data modeling, pipeline reliability, observability, testing, and documentation — by building the reference implementations yourself.

  • Optimize platform performance and manage costs across Snowflake compute, Databricks clusters, and Fabric capacities through hands-on tuning and configuration.

  • Implement data governance and security controls across engineering initiatives, ensuring compliance, data quality, and lineage traceability at the code and platform level.

  • Stay current with the evolving data platform landscape and evaluate emerging tools through hands-on proof-of-concept work, not just vendor demos.

Preferred Qualifications:

  • A career path that shows sustained, hands-on implementation even as responsibilities grew — architect-turned-manager, not analyst-turned-manager.

  • Hands-on experience building medallion-architecture lakehouses on Databricks or Microsoft Fabric, and designing and tuning performant Snowflake warehouse structures.

  • Experience implementing CI/CD for data pipelines using Azure DevOps or GitHub Actions, including writing the pipeline definitions and deployment scripts yourself.

  • Familiarity with dbt for transformation orchestration, with personal experience writing and maintaining dbt models and tests.

  • Exposure to real-time or near-real-time data ingestion patterns (Kafka, Event Hubs, Spark Structured Streaming) through direct implementation.

  • Experience with data mesh or domain-oriented data platform strategies is a strong plus.

  • Strong communication skills with the ability to articulate architectural trade-offs to both technical and non-technical audiences.

  • Excellent stakeholder management skills, with experience running sprint planning, design reviews, and cross-functional delivery coordination.

About Riveron:

At Riveron, we partner with clients—from global multinationals to high-growth private entities—to solve complex finance challenges, guided by our DELTA values: Drive, Excellence, Leadership, Teamwork, and Accountability. Our entrepreneurial culture thrives on collaboration, diverse perspectives, and delivering exceptional outcomes. We are committed to fostering growth, both for our clients and our people, through mentorship, integrity, and a client-centric approach. This inclusive environment offers flexibility, progressive benefits, and meaningful opportunities for impactful work that supports well-being in and out of the office.

Want to stay connected with Riveron? Join our Talent Community to learn more about our growing firm and be considered for future opportunities.

Check us out on social media:

LinkedIn Glassdoor Instagram Facebook

Riveron Consulting is an Equal Opportunity Employer and believes that we are stronger together through our diversity. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, disability status, protected veteran status, sexual orientation, gender identity or any other characteristic protected by law.

Full time roles are eligible for a full range of benefits including medical, dental, and vision insurance, 401(k) with company match, and PTO. A complete description of all available benefits can be found at Riveron's Benefits page at https://riveron.com/riveron-life/. Contract roles are not eligible for benefits.

Fraud Alert

Please beware of fraudulent schemes or impersonations when going through the job application process. A Riveron employee will never recruit via text or extend unsolicited employment offers. Additionally, a Riveron employee will never ask you to exchange money or purchase anything as part of the recruiting process.

Artificial intelligence (AI) tools are used to support the hiring process in screening, assessing, and/or selecting applicants for this position. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Your next step

  • Have your CV and examples of relevant work ready.
  • Check the listed location, eligibility and core experience before starting.
  • Ask the employer about the salary range before committing time to the process.

Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.

Already applied? Track this application

Source & posting history

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

No pay amount identified in the saved description.

Location & working pattern

India

Working pattern and location restrictions need checking in the full posting.

Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

Status in our records
Active
First seen by us
Jun 2, 2026
Recorded sightings
174
Last seen by us
Oct 8, 2026
Employer says posted
Dec 8, 2025

These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.

Report an error

See how this role fits your experience

Add your resume to compare the role’s scope, tools and requirements with your experience.

Find answers in the posting

AI
How answers work

AI selects complete passages from this posting. Check them for conditions and exceptions.

Uses this posting and your question. No profile needed.