Skill required: Data Management - Structured Query Language (SQL)

Designation: Data Eng, Mgmt & Governance Sr Analyst

Qualifications:BE/BTech

Years of Experience:5 to 8 years

About Accenture

Accenture is a global professional services company with leading capabilities in digital, cloud and security.Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Technology and Operations services, and Accenture Song— all powered by the world's largest network of Advanced Technology and Intelligent Operations centers. Our 784,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities.Visit us at www.accenture.com

What would you do Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Technology and Operations services, and Accenture Song— all powered by the world's largest network of Advanced Technology and Intelligent Operations centers. Our 699,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Visit us at www.accenture.com. The Senior Analyst – Data Engineering will be a key contributor in designing and building reliable data pipelines, writing optimized SQL queries, and developing Python-based automation scripts that support business reporting and analytics. This role bridges raw data sources and actionable insights — ensuring data accuracy, consistency, and timely availability for downstream consumption. The individual will work closely with senior team members, stakeholders, and cross-functional teams to understand data requirements, implement efficient data processing workflows, and maintain high standards of data quality. They will also actively contribute to continuous improvement initiatives and support junior team members in skill development.

What are we looking for

4–6 years in Data Engineering, Data Analytics, ETL development, or a similar role
Strong SQL proficiency — joins, subqueries, CTEs, window functions, stored procedures, views, basic query optimization
Solid scripting skills — Pandas, NumPy, SQLAlchemy; ability to build data processing and automation scripts
Good understanding of relational databases, data structures, normalization, and dimensional modeling concepts
Familiarity with data validation, reconciliation techniques, and quality assurance practices
Analytical mindset with attention to detail and ability to troubleshoot data issues independently
Good proficiency in English (spoken & written); ability to articulate technical concepts to non-technical stakeholders
Bachelor s degree in engineering, Computer Science, Data Science, Information Technology, or equivalent
Exposure to Azure (Data Factory, Databricks), AWS (S3, Glue), or GCP (BigQuery)
Hands-on experience with Alteryx, SSIS, Matillion, or Airflow
Basic working knowledge of Power BI or Tableau for data validation and visualization
Familiarity with Git or Azure DevOps for code management
Exposure to Agile/Scrum delivery methodologies
Prior experience in Marketing, Finance, or Supply Chain data is a plus
Familiarity with Power Automate, VBA, or scheduling tools for workflow automation Roles and Responsibilities:
Develop and maintain ETL/ELT pipelines using SQL and Python to extract, transform, and load data from multiple source systems
Write well-structured, performant SQL queries — including joins, subqueries, CTEs, window functions, stored procedures, and views for data extraction and transformation
Build Python scripts (using Pandas, NumPy, SQLAlchemy) for data cleansing, transformation, validation, and automation of repetitive tasks
Support integration of multiple data sources into consolidated data models for reporting and visualization
Assist in identifying optimal data storage solutions and contribute to architecture discussions
Execute data validation and reconciliation checks to ensure completeness, accuracy, and consistency of processed data
Identify and flag data quality issues proactively; work with the team to implement corrective measures
Maintain documentation of data flows, transformation logic, and quality checkpoints
Support adherence to data governance standards and established operating models
Collaborate with business stakeholders and project leads to understand data requirements and translate them into technical tasks
Participate in requirement gathering sessions, feasibility discussions, and solution walkthroughs
Provide regular status updates on assigned deliverables and flag blockers or risks early
Support client-facing interactions under the guidance of senior team members — assisting with data queries, status reporting, and demos
Identify opportunities to automate manual processes, reduce errors, and improve efficiency in day-to-day workflows
Contribute to standardization of templates, coding practices, and reusable components across the team
Stay current with emerging tools, techniques, and industry best practices in data engineering
Actively participate in knowledge-sharing sessions, team brown bags, and CoE-level forums
Support and guide junior team members (Analysts) on SQL best practices, Python scripting, and data quality processes
Contribute to onboarding new team members by sharing process documentation and technical context
Take ownership of assigned modules/deliverables with minimal supervision, BE,BTech