Deloitte | India Offices of the US

Assistant Manager

Shift timings: 2:00 PM to 11:00 PM

Work location: HYD

Data Integration and Quality Specialist - Assistant Manager

Are you a detailed oriented, data driven and desire to excel? Data Integration and Architecture is the hub for Deloitte Talent Data, helping the US and US India Offices with Talent data and analytical needs. The data we provide, and our consultative services are used by different groups of the firm to drive people strategy in headcount forecast, retention, hiring, compensation, learning and resource management. 

Work you’ll do

The Data Integration and Quality Specialist - Assistant Manager plays a pivotal role in understanding the design, implementation and managing the organization’s data integration solutions and architectural frameworks. This position supports the development of scalable, secure, and efficient data environments that enable seamless data flow across systems and support business intelligence, analytics, and digital transformation initiatives. This role ensures that data across the enterprise is accurate, consistent, reliable, and fit for business use. You will collaborate with cross-functional teams, leverage modern data quality tools, and stays abreast of evolving data management trends and regulatory requirements.

Key Responsibilities

• Data Modeling & Integration

o Ability to document current design and proposed user driven efficiency changes to data models, aligned with the business priorities.

o Collaborate with business and IT teams to gather integration requirements and translate them into technical solutions.

Data Integration and Automation

o Support the development and maintenance of enterprise data architecture, including data models, data flows, and data storage solutions.

o Implement innovative ways to monitor the consistency of the data throughout the lifecycle, ingestion, transformation and synchronization

o Responsible for automating data integration workflows and adopting DataOps practices for agility and quality

o Data Governance & Compliance

o Ensure architectural solutions align with data governance, security, and compliance standards.

o Document data architecture designs, standards, and best practices.

o Monitor and troubleshoot data integration jobs, ensuring high availability and performance.

Cloud & Modern Data Platforms

o Ability to adopt and work on cloud-based integration tools and serverless architectures.

o Drive towards unified data platforms that combine data integration, storage, and analytics.

o Participate in the selection and implementation of data platforms, tools, and technologies.

o Challenge the technology systems approach and their interlinkages in order to determine the best way of data flow between the systems, as well as to be able to extract data using relevant extraction rules.

o Operationalizing analytics: Ability to process the embedding analytical insights, models, or data-driven decisions directly into business processes, systems, and workflows—so that analytics move beyond dashboards and reports to drive real-time, automated actions within the organization. 

• Data Quality Management

o Leverage Generative AI: AI & Automation in Data Quality: knowledge and ability to use machine learning and AI-driven tools for automated data profiling, anomaly detection, and data cleansing.

o Data Observability: Goes beyond traditional data quality by providing end-to-end visibility into data pipelines, monitoring data health, lineage, and detecting issues proactively

o Data Mesh & Decentralized Data Ownership: Growing adoption of data mesh principles, empowering domain teams to own and manage data quality.

o Cloud-Native Data Quality Solutions: Shift toward cloud-based data quality and governance platforms for scalability and integration.

o Real-Time Data Quality Monitoring: Demand for real-time or near-real-time data quality monitoring to support agile business decisions.

o Data Privacy & Regulatory Compliance: Heightened focus on data quality as a foundation for privacy compliance (GDPR, CCPA, etc.).

o Data Literacy Initiatives: Emphasis on upskilling teams in data quality concepts and best practices.

Education, Technical Skills and Experience

• Graduate/Masters degree required Minimum of 7-9 years applied experience in data systems, architecture, data flows and integrations. 

• Advanced proficiency in SQL and Microsoft tools like Excel (including VBA), word, PowerPoints with strong qualitative and analytical skills.

• Working experience or knowledge on tools like SABA, MySource, Success Factor, SAP Analysis for Office (AO), Crystal Reports, and SQL Studio. 

• Familiarity with data visualization tools such as Tableau, QlikView, and Power BI for creating internal dashboards

• Prefer Working experience or Knowledge on Data Quality tools like SAP Information Steward and Microsoft SQL Server- Data Quality Services (DQS)

• Ability to manage ServiceNow Intake process, Smartsheet and Azure Ops for tracking 

Interpersonal Skills

• Strong oral and written communication skills for clarifying data requests, escalating data issues, and writing analyses.

• Ability to work independently and collaboratively within a team.

• Confidence, responsiveness, flexibility, initiative, and sound decision-making.

• Strong interpersonal skills to build effective working relationships.

• Ability to quickly learn and adapt to new processes, tools, and technologies.

• Resourcefulness and creativity in problem-solving, especially in a changing environment.

• Keen focuses on data accuracy and meticulous attention to detail.