MIS Manager
Role Overview
Develop, maintain, and operate business-focused data marts and automated data pipelines that support management reporting, customer analytics, marketing campaigns, and self-service analytics. Work closely with business stakeholders, Data Engineers, Data Analysts, and Data Scientists to translate business requirements into accurate, consistent, and reusable data solutions.
Job Responsibilities
Work closely with business stakeholders to understand objectives, define KPIs, clarify business rules, and translate requirements into data models, transformation logic, and acceptance criteria.
Design, develop, and maintain data marts and analytical datasets covering customer profiles, transactions, loyalty programs, tenant performance, and marketing campaigns.
Build and maintain automated ETL/ELT pipelines using SQL, Python, and appropriate data-processing tools, including job scheduling, dependency management, monitoring, and rerun procedures.
Establish data quality checks for completeness, accuracy, uniqueness, consistency, and freshness. Investigate discrepancies by tracing data from business reports through transformation logic to source systems.
Collaborate with the Data Engineering team to resolve upstream data issues, assess source changes, and maintain consistent definitions and reliable data integration across systems.
Optimize SQL queries and data-processing workflows to improve performance, scalability, and operational reliability. Troubleshoot failed jobs and coordinate incident resolution.
Manage changes to data models, source mappings, and historical datasets, including impact assessment, testing, data reconciliation, backfills, and controlled deployment.
Partner with Data Analysts, Data Scientists, and marketing teams to prepare campaign audiences, customer segmentation datasets, and data for campaign performance and ROI analysis.
Deliver reusable datasets and automated outputs for management reporting, ad hoc analysis, and self-service analytics.
Maintain clear documentation of business rules, data lineage, dependencies, and operational procedures, while following organizational data security and access-control policies.
Review technical work, prioritize stakeholder requests, and monitor delivery quality, data accuracy, and pipeline reliability.
Required Qualifications
Bachelor’s or master’s degree in Management Information Systems, Computer Science, Information Technology, Data Engineering, or a related field.
At least 5–7 years of relevant experience in MIS, data engineering, analytics engineering, data warehousing, or business data solutions, with experience leading projects or teams.
Strong hands-on SQL skills, including complex transformations, joins, aggregations, window functions, and query performance optimization.
Proficiency in Python for data processing, automation, validation, and system integration.
Practical experience designing data models and developing ETL/ELT pipelines and business-oriented data marts.
Experience operating scheduled production workflows using Apache Airflow or a similar orchestration tool, including troubleshooting and dependency management.
Strong analytical and problem-solving skills, with experience investigating data quality issues and reconciling results across multiple sources.
Ability to translate business requirements into technical solutions and explain data issues, assumptions, and trade-offs clearly to nontechnical stakeholders.
Familiarity with Git, code review, testing, and controlled deployment practices.
Preferred Qualifications
Experience with PySpark, distributed data processing, cloud data warehouses, object storage, and Parquet datasets.
Experience developing dashboards or self-service analytics using Power BI, Tableau, QlikView, or similar tools is a plus.