Sr Data Engineer, Data Eng & Governance

Dolby; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Dolby is seeking a Senior Data Engineer to design, build, and maintain robust data pipelines and infrastructure powering our data-driven initiatives. You will work with large datasets, cloud services, and modern frameworks to enable real-time insights across products and user experiences.

You will collaborate with AI researchers, data scientists, and product teams, implement monitoring, data quality, and governance, and mentor junior engineers while advancing our data platform’s scalability and

Qualifications

• 5+ years of data engineering experience with large-scale pipelines.
• Expertise in Python/Scala/Java and distributed processing.
• Master's degree in CS/DS or related field.

Responsibilities

• Build, test, and maintain large-scale data pipelines and infrastructure.
• Develop tooling and APIs for self-service data access.
• Design scalable data platforms with monitoring and cost efficiency.
• Collaborate with AI researchers, data scientists, and product teams.
• Mentor junior data engineers and contribute to best practices.

Skills

Python

Scala

Java

Apache Spark

Apache Beam

SQL

Databricks

Snowflake

BigQuery

AWS

GCP

Azure

Docker

Kubernetes

CI/CD

Relational DBs

NoSQL

Data Modeling

Data Architecture

Communication

Education

Master’s degree in Computer Science, Data Science, or related field

Job description

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We are seeking a highly skilled and experienced Senior Data Engineerto join our growing data team. In this role, you will be instrumental indesigning, building, and maintaining robust and scalable data pipelinesand infrastructure that power critical data-driven initiatives across ourorganization. You will work with vast datasets, cutting-edgetechnologies, and collaborate closely with AI researchers, other dataengineers, data scientists, machine learning engineers, and productteams to deliver insights that shape the future of our products and userexperiences.

What You'll Do:

Build and Optimize Data Infrastructure:

• Develop, construct, test, and maintain large-scale data ingest architecture consisting of diverse cloud-based services (messaging,storage, Kubernetes, persistent data store, serverless functions, etc).
• Create tooling like SDK, APIs to enable user self-service.
• Contribute to the design and evolution of our core data platform,ensuring its scalability, reliability, and cost-effectiveness.
• Implement robust monitoring, alerting, and logging solutions for datapipelines and infrastructure to proactively identify and resolve issues.

Design and Implement Scalable Data Pipelines:

• Design and implement highly reliable and efficient ETL/ELT processes to ingest, transform, and load data from diverse sources (e.g., real-time events, third-party APIs, rich media datasets) into our data lake and data warehouses.
• Utilize distributed data processing frameworks like Spark or similar to handle large scale data volumes with high throughput and low latency.

Ensure Data Quality and Governance:

• Describe and annotate datasets using industry standard schemasand internal specifications
• Cultivate data catalogs and metadata management solutions to i.o. improve data discoverability and understanding across theorganization.
• Implement data validation, cleansing, and reconciliation processes toensure the accuracy and integrity of our data assets.

Collaborate and Mentor:

• Work closely with stakeholders (research, engineering, product, andpeers more broadly) to translate their data needs into robust datasolutions.
• Provide technical leadership and mentorship to junior data engineers,fostering a culture of technical excellence and continuous learning.
• Contribute to the evolution of our data architecture and engineering bestpractices.

What You’ll Bring:

• Extensive Experience: 5+ years of experience in data engineering,with a strong focus on building and maintaining large-scale datapipelines and infrastructure.
• Programming Proficiency: Expert-level proficiency in at least onemajor programming language such as Python, Scala, or Java.- Distributed Data Processing: Deep experience with distributed dataprocessing frameworks (e.g., Apache Spark, Apache Beam). Strongfoundation in event-based approaches and systems includingmessaging/topics, pub/sub, queues, etc.
• Data Warehousing/Lakes: Hands-on experience with datawarehousing solutions (e.g., Databricks, Snowflake, Redshift,BigQuery) and data lake technologies (e.g., S3, HDFS). Deepexperience with managing large scale, heterogeneous datasets onDatabricks is highly preferred.
• SQL Mastery: Advanced SQL skills for data manipulation, analysis,and optimization.
• Cloud Platforms: Strong experience with one or more major cloudproviders (AWS, GCP, Azure) and their data-related services.
• Orchestration and DevOps: Familiarity with containerization andorchestration technologies (e.g., Docker, Kubernetes). Proficient atCI/CD-based deployment.
• Database Knowledge: Solid understanding of relational and NoSQLdatabases.
• Data Modeling: Expertise in data modeling, schema design, and dataarchitecture principles.
• Problem-Solving: Excellent analytical and problem-solving skills, witha track record of tackling complex data challenges.
• Communication: Strong communication and interpersonal skills, withthe ability to collaborate effectively with cross-functional teams.
• Master’s degree in Computer Science, Data Science, or a relatedquantitative field.

Bonus Points:

• Strong theoretical understanding of distributed computing conceptssuch as concurrency, parallelism, queueing, consistency,coordination protocols, etc.-Experience with machine learning pipelines and MLOps principles.
• Contributions to open-source data projects.