Required Skills/Competencies

- Expert-level command of advanced SQL development, query optimization, database design, indexing strategies, performance tuning, and relational database management systems, with the ability to set standards and review the work of others.
- Expert-level experience with cloud platforms such as Microsoft Fabric, Azure, Google Cloud Platform, Databricks or AWS including cloud-native data services, and the ability to define enterprise cloud data strategy.
- Expert-level experience with Infrastructure as Code (IaC) technologies such as Terraform, ARM Templates, CloudFormation, or equivalent, including ownership of enterprise IaC standards.
- Expert-level experience with containerization and orchestration technologies including Docker, Kubernetes, OpenShift, and container platform administration at enterprise scale.
- Expert-level experience architecting, developing, and maintaining scalable ETL/ELT pipelines using tools such as Fabric Data Factory, Azure Data Factory, Databricks, Dataflow, Dataproc, Apache Spark, Apache Airflow, Informatica, SSIS, or similar technologies.
- Expert-level experience with modern data lake architecture, Lakehouse platforms, Medallion Architecture, Delta Lake, Parquet, and enterprise data warehousing concepts, including ownership of enterprise architecture standards.
- Expert-level experience with batch and real-time/streaming ingestion frameworks including Kafka, NiFi, Azure Event Hub, Kinesis, Spark Streaming, Structured Streaming, Flink, or similar technologies.
- Expert-level experience in data modeling techniques including conceptual, logical, and physical modeling, dimensional modeling, star schema, snowflake schema, and Data Vault methodologies, with the ability to arbitrate modeling standards across teams.
- Expert-level experience with API integrations, REST services, GraphQL, data exchange protocols, webhooks, and enterprise system integrations.
- Expert-level command of Linux environments, shell scripting, automation, process monitoring, job scheduling, and system troubleshooting.
- Expert-level experience with Git, GitHub, GitLab, Azure DevOps, CI/CD pipelines, code versioning, automated deployments, and DevSecOps practices, including ownership of engineering standards.
- Expert-level proficiency in Python, Scala, Java, or similar programming languages for data engineering and automation development.
- Expert-level experience with big data technologies such as Hadoop, Spark, Hive, Delta Lake, Iceberg, HBase, or comparable technologies.
- Advanced knowledge of data governance, data quality management, metadata management, data lineage, master data management, and regulatory compliance, with the ability to define governance strategy.
- Advanced understanding of data security principles, encryption, role-based access controls, identity management, and cloud security practices.
- Demonstrated experience architecting scalable, resilient, and high-performance enterprise data architectures spanning multiple business units or sites.
- Excellent communication skills with proven experience explaining complex technical concepts, architecture decisions, and platform strategy to executive, technical, and non-technical stakeholders.
- Demonstrated ability to collaborate with and influence business users, analytics teams, data scientists, architects,



and cross-functional technology organizations at a senior level.
- Demonstrated project management and technical leadership of large, complex, cross-functional and/or cross-site data engineering initiatives.
- Demonstrated technical leadership and people management of data engineering teams, including hiring, mentoring, performance management, and career development.

Essential Responsibilities 1. Data Platform Engineering & Development

Data Architecture and Solution Design

- Partners with senior business partners, analytics leadership, and enterprise architects to define enterprise data strategy and scope high-impact data engineering initiatives.
- Owns the design and implementation of enterprise-scale data platforms, data lakehouses, and cloud-native architectures across multiple domains or sites.
- Defines and governs data integration strategies, architecture standards, and platform best practices for the broader organization.
- Directs the development of scalable data solutions supporting reporting, analytics, machine learning, and operational applications.

Data Pipeline Development

- Directs the design, development, and maintenance of complex, enterprise-scale batch and streaming ingestion pipelines.
- Oversee ELT/ETL processes integrating data from enterprise systems, manufacturing platforms, APIs, IoT sources, and external vendors.
- Establishes reusable frameworks, templates, and automation solutions adopted across the data engineering organization to accelerate delivery.
- Guides and reviews the development of robust data transformation logic using SQL, Spark, Python, Scala, or equivalent technologies.

Data Modeling and Storage

- Owns conceptual, logical, and physical data models supporting enterprise business processes across multiple domains.
- Directs the development of dimensional models and enterprise data warehouse solutions.
- Sets standards for and oversees implementation of Medallion Architecture layers (Bronze, Silver, Gold) across the enterprise analytics estate.

•

Cloud Engineering & Infrastructure

- Owns the design and management of cloud infrastructure supporting enterprise data platforms across multiple sites.
- Directs enterprise-wide adoption of Infrastructure as Code (Terraform or equivalent) for platform provisioning and management.
- Oversee deployment and management of cloud-native services, containerized solutions, and Kubernetes environments at scale.

Data Quality & Reliability

- Establishes enterprise-wide data quality controls, validations, and monitoring frameworks.
- Directs investigation of complex, cross-platform data quality issues and oversees corrective action plans.
- Owns the design and governance of observability solutions for pipelines and platforms across the organization.
- Sets standards for automated alerting, monitoring, logging, and operational dashboards.

2. Platform Operations, Consultation & Implementation

Technical Leadership





- Serves as the senior data engineering subject matter expert and technical authority for the organization.
- Leads and facilitates technical design discussions and architecture reviews for enterprise-wide strategic data initiatives.
- Provides guidance, oversight, and technical direction to Data Engineers and Data Engineering III staff.
- Owns peer review processes for data engineering methodologies, architecture designs, and implementation patterns.

Project Leadership

- Leads large and complex data engineering projects and major components of enterprise business initiatives, end to end.
- Collaborates with and influences Data Science, Analytics, Manufacturing, Finance, Supply Chain, and Information Systems leadership.
- Owns the development of implementation plans and multi-year technical roadmaps.

DevOps & Automation

- Directs the build-out of CI/CD pipelines supporting automated testing and deployment processes at enterprise scale.
- Owns infrastructure automation strategy and release management practices for the organization.

Communication & Stakeholder Engagement

- Effectively communicates platform architecture, technical risks, and implementation recommendations to senior business partners and leadership.
- Owns the adoption and operationalization strategy for new data platforms and engineering solutions across the organization.
- Partners with senior stakeholders define critical enterprise data needs and requirements.
- Directs technical training, documentation, and knowledge transfer programs across the engineering organization.

3. Team & People Management

Team Leadership & Development

- Manages, mentors, and develops a team of data engineers, acting as their direct people manager.
- Owns hiring, onboarding, and performance management for the data engineering team, including goal setting and performance reviews.
- Identifies training needs and develops and delivers technical training programs for engineers, analysts, and other technical staff.
- Sets individual and team goals aligned with platform strategy and provides regular coaching and career development guidance.
- Fosters a culture of technical excellence, collaboration, psychological safety, and continuous learning within the team.

Cross-Functional & Cross-Site Leadership

- Leads cross-functional and cross-site project teams to deliver large, complex data engineering initiatives.

4. Industry Research & Innovation

- Research and maintains deep awareness of emerging technologies, cloud services, and industry best practices.
- Evaluates new data engineering tools, frameworks, and architectural patterns and determines organizational fit.
- Proactively introduces innovative solutions to improve scalability, performance, reliability, and maintainability of enterprise data platforms.
- Research advancements in cloud computing, Data Lakehouse architectures, distributed computing, streaming analytics, and up-to-date data platform technologies.
- Recommends and helps set strategic technology direction supporting organizational growth and digital transformation initiatives.
- Builds technical capability within the team by introducing emerging tools and frameworks and mentoring engineers on new technologies.
- Contributes to and helps own enterprise technology standards and long-term data platform strategy.

📌 Data Manager (Chennai)
🏢 connectrz
📍 Chennai