Job Description
As a Senior Manager Data Engineering, your role will involve leading and managing data engineering teams to build robust, scalable, and secure data infrastructure using cloud technologies across Azure, AWS, Google Cloud Platform (GCP), Informatica, and Oracle Cloud. You will need to combine strong technical expertise with leadership skills to drive data platform initiatives, optimize data pipelines, and collaborate cross-functionally to support analytics, BI, and data science needs.
• *Key Responsibilities:**
- Lead and mentor data engineering teams to design, build, and maintain high-performance, scalable data platforms on cloud environments including Azure, AWS, Google Cloud Platform (GCP), Informatica, and Oracle Cloud.
- Own the end-to-end lifecycle of data engineering solutions: data ingestion, transformation, storage, and delivery.
- Collaborate with enterprise architects, data scientists, business analysts, and IT teams to translate business requirements into scalable data engineering solutions.
- Drive adoption of best practices in cloud-native data architectures such as data lakes, data warehouses, and lakehouses.
- Define technical standards, architecture guidelines, and operational procedures to ensure reliability, performance, and data security.
- Oversee cloud migration projects and upgrade efforts for existing on-premises data platforms to cloud.
- Manage vendor relationships and evaluate emerging cloud services and tools relevant to data engineering.
- Implement and enforce data governance, data quality, and compliance measures.
- Monitor data pipeline performance and implement tuning, troubleshooting, and capacity planning.
- Lead Agile delivery processes with efficient sprint planning, backlog grooming, and stakeholder communication.
- Provide technical leadership in evaluating and incorporating automation and orchestration tools (e.g., Apache Airflow, Jenkins).
- Report progress, risks, and KPIs to senior leadership and stakeholders.
• *Must-Have Skills & Qualifications:**
• *Cloud Platforms Expertise:**
- Demonstrated hands-on experience designing and managing data workloads across Microsoft Azure, AWS, Google Cloud Platform, and Oracle Cloud Infrastructure.
- Experience in cloud migration from traditional on-prem data systems to cloud-native data platforms.
• *Data Engineering Fundamentals:**
- Deep understanding of ETL/ELT pipelines, data ingestion, batch and streaming data processing.
- Expertise in data storage solutions: Data Lakes, Data Warehouses, Lakehouses, and relational/non-relational databases.
- Skilled in programming/scripting languages such as Python, SQL, Scala, or Java.
- Experience with big data frameworks and tools such as Apache Spark, Kafka, and Apache Airflow.
- Familiarity with containerization (Docker) and orchestration (Kubernetes) in data workflows.
- Knowledge of infrastructure automation tools like Terraform, CloudFormation, and ARM templates.
- Strong grasp of data governance, security frameworks, and regulatory compliance.
• *Leadership & Management:**
- Proven experience leading and scaling technical teams in data engineering or related fields.
- Strong project management and Agile methodology experience.
- Ability to effectively communicate technical concepts to business and technical stakeholders.
- Skilled in resource planning, budgeting, and vendor management.
• *Preferred Qualifications:**
- Bachelors or Masters degree in Computer Science, Engineering, Data Science, or related disciplines.
- Relevant cloud certifications such as Microsoft Certified: Azure Data Engineer Associate, AWS Certified Big Data Specialty or Data Analytics Specialty, Google Professional Data Engineer, Oracle Cloud Infrastructure Data Management Specialist.
- Experience working in multi-cloud or hybrid cloud environments.
- Knowledge of orchestration of ML pipelines and basic AI/ML concepts is a plus. As a Senior Manager Data Engineering, your role will involve leading and managing data engineering teams to build robust, scalable, and secure data infrastructure using cloud technologies across Azure, AWS, Google Cloud Platform (GCP), Informatica, and Oracle Cloud. You will need to combine strong technical expertise with leadership skills to drive data platform initiatives, optimize data pipelines, and collaborate cross-functionally to support analytics, BI, and data science needs.
• *Key Responsibilities:**
- Lead and mentor data engineering teams to design, build, and maintain high-performance, scalable data platforms on cloud environments including Azure, AWS, Google Cloud Platform (GCP), Informatica, and Oracle Cloud.
- Own the end-to-end lifecycle of data engineering solutions: data ingestion, transformation, storage, and delivery.
- Collaborate with enterprise architects, data scientists, business analysts, and IT teams to translate business requirements into scalable data engineering solutions.
- Drive adoption of best practices in cloud-native data architectures su
• *Key Responsibilities:**
- Lead and mentor data engineering teams to design, build, and maintain high-performance, scalable data platforms on cloud environments including Azure, AWS, Google Cloud Platform (GCP), Informatica, and Oracle Cloud.
- Own the end-to-end lifecycle of data engineering solutions: data ingestion, transformation, storage, and delivery.
- Collaborate with enterprise architects, data scientists, business analysts, and IT teams to translate business requirements into scalable data engineering solutions.
- Drive adoption of best practices in cloud-native data architectures such as data lakes, data warehouses, and lakehouses.
- Define technical standards, architecture guidelines, and operational procedures to ensure reliability, performance, and data security.
- Oversee cloud migration projects and upgrade efforts for existing on-premises data platforms to cloud.
- Manage vendor relationships and evaluate emerging cloud services and tools relevant to data engineering.
- Implement and enforce data governance, data quality, and compliance measures.
- Monitor data pipeline performance and implement tuning, troubleshooting, and capacity planning.
- Lead Agile delivery processes with efficient sprint planning, backlog grooming, and stakeholder communication.
- Provide technical leadership in evaluating and incorporating automation and orchestration tools (e.g., Apache Airflow, Jenkins).
- Report progress, risks, and KPIs to senior leadership and stakeholders.
• *Must-Have Skills & Qualifications:**
• *Cloud Platforms Expertise:**
- Demonstrated hands-on experience designing and managing data workloads across Microsoft Azure, AWS, Google Cloud Platform, and Oracle Cloud Infrastructure.
- Experience in cloud migration from traditional on-prem data systems to cloud-native data platforms.
• *Data Engineering Fundamentals:**
- Deep understanding of ETL/ELT pipelines, data ingestion, batch and streaming data processing.
- Expertise in data storage solutions: Data Lakes, Data Warehouses, Lakehouses, and relational/non-relational databases.
- Skilled in programming/scripting languages such as Python, SQL, Scala, or Java.
- Experience with big data frameworks and tools such as Apache Spark, Kafka, and Apache Airflow.
- Familiarity with containerization (Docker) and orchestration (Kubernetes) in data workflows.
- Knowledge of infrastructure automation tools like Terraform, CloudFormation, and ARM templates.
- Strong grasp of data governance, security frameworks, and regulatory compliance.
• *Leadership & Management:**
- Proven experience leading and scaling technical teams in data engineering or related fields.
- Strong project management and Agile methodology experience.
- Ability to effectively communicate technical concepts to business and technical stakeholders.
- Skilled in resource planning, budgeting, and vendor management.
• *Preferred Qualifications:**
- Bachelors or Masters degree in Computer Science, Engineering, Data Science, or related disciplines.
- Relevant cloud certifications such as Microsoft Certified: Azure Data Engineer Associate, AWS Certified Big Data Specialty or Data Analytics Specialty, Google Professional Data Engineer, Oracle Cloud Infrastructure Data Management Specialist.
- Experience working in multi-cloud or hybrid cloud environments.
- Knowledge of orchestration of ML pipelines and basic AI/ML concepts is a plus. As a Senior Manager Data Engineering, your role will involve leading and managing data engineering teams to build robust, scalable, and secure data infrastructure using cloud technologies across Azure, AWS, Google Cloud Platform (GCP), Informatica, and Oracle Cloud. You will need to combine strong technical expertise with leadership skills to drive data platform initiatives, optimize data pipelines, and collaborate cross-functionally to support analytics, BI, and data science needs.
• *Key Responsibilities:**
- Lead and mentor data engineering teams to design, build, and maintain high-performance, scalable data platforms on cloud environments including Azure, AWS, Google Cloud Platform (GCP), Informatica, and Oracle Cloud.
- Own the end-to-end lifecycle of data engineering solutions: data ingestion, transformation, storage, and delivery.
- Collaborate with enterprise architects, data scientists, business analysts, and IT teams to translate business requirements into scalable data engineering solutions.
- Drive adoption of best practices in cloud-native data architectures su
💡 Quick Summary
Seeking a career-building opportunity? The Senior Data Engineering Manager position is now open for candidates interested in the Media & Communication sector. This role in Pune offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Media & Communication is a plus.
