Data Engineering Manager

at Riveron

India; Pune, IN; Bangalore, IN, remote, manager, full time, 8–12 years

posted 08 Dec 2025

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Spark Structured Streaming
Infrastructure as Code
Kafka
Azure SQL Database
Azure Data Lake Storage
SQL
Azure Data Factory
Git
GitHub Actions
Microsoft Fabric
Azure Synapse Analytics
Event Hubs
Snowflake
dbt
CI/CD
Databricks
Spark
Azure DevOps
Python

About the role

Remote: yes Posted: 2025-12-08

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.

What you would 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.

Read from jobs.ashbyhq.com, last seen 4d ago.

What they are looking for

- A data engineering professional with 8 to 12 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.

- 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.