Key Responsibilities:

Governance & Compliance: Support data, compliance, and environment governance processes for the assigned domain. Facilitate an intake process to reduce rework and promote reuse of analytics solutions.
Data Integration & Cleansing: Collaborate with peers to integrate data from warehouses, data lakes, and other source systems. Cleanse data to ensure report accuracy and reduce duplication, maintaining documentation of the cleansing process.
Analytics Projects: Support analytics projects to produce insights for business decision-making. Deliver results using reports, business intelligence technology, or other appropriate mechanisms.
Standards & Guidelines: Support the business in establishing and enforcing guidelines and standards for data collection, integration, and processes.
Data Ingestion: Support project work to ingest key data into the data lake, ensuring the creation and maintenance of relevant metadata and data profiles.
Stakeholder Assistance: Assist stakeholders and business teams with finding necessary and relevant data. Participate in communities of practice promoting the responsible use of analytics.
Training & Communication: Coach and assist business users in developing capabilities to conduct work in the analytics ecosystem. Assist with the preparation of communications to leaders and stakeholders.

Data Analytics & Visualization:

Design, develop, and maintain interactive dashboards and reports using Power BI to provide actionable insights.
Create and optimize data models to support visualization and analytics needs.
Implement statistical models and techniques to analyze business data and derive meaningful insights.

ETL & Data Management:

Design and develop ETL pipelines to extract, transform, and load data from various sources.
Ensure data accuracy, integrity, and consistency across different platforms.
Work with relational databases, writing and optimizing SQL queries for data processing.

Application Development & Automation:

Develop applications and process automation using Power Apps to streamline business processes.
Integrate Power Apps with Power BI, SharePoint, and other enterprise systems.

Team Leadership & Project Management:

Lead a team of data analysts and data engineers, providing technical guidance and support.
Manage project timelines and deliverables, ensuring the successful execution of data-driven initiatives.

Advanced Analytics & Platform Expertise:

Leverage Databricks and PySpark to perform advanced analytics, build scalable data pipelines, and optimize data workflows.
Utilize statistical and machine learning models to solve complex business problems.
Explore and implement solutions using low-code/no-code platforms such as Mendix (preferred).

Experience: 6-8 years of relevant work experience required. Experience in manufacturing analytics or a related domain is preferred.

Skills:

Qualifications:

Bachelor s degree in Computer Science, Information Technology, or a related field.

Competencies:

Collaborates: Building partnerships and working collaboratively with others to meet shared objectives.
Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.
Customer Focus: Building strong customer relationships and delivering customer-centric solutions.
Interpersonal Savvy: Relating openly and comfortably with diverse groups of people.
Data Analytics: Discovering, interpreting, and communicating qualitative and quantitative data to enable data-driven business decisions.
Data Mining: Extracting insights from data by identifying relationships and patterns.
Data Modeling: Creating, writing, and testing data models to meet business, technical, security, governance, and compliance requirements.
Data Communication and Visualization: Constructing a narrative of the business problem, root cause, solution options, and opportunities through data visualization.
Data Literacy: Expressing data in context, including data sources and constructs, analytical methods, and applied techniques.
Data Profiling: Assessing data issues and cleansing requirements to ensure quality data.
Data Quality: Identifying, understanding, and correcting flaws in data to support effective information governance.
Values Differences: Recognizing the value that different perspectives and cultures bring to an organization.

Role: Database Analyst

Industry Type: Automobile

Department: Engineering - Software & QA

Employment Type: Full Time, Permanent

Role Category: DBA / Data warehousing

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