The Data Analytics Manager will lead our reporting ecosystem and champion data governance across the organization. In this role, you will be the driving force behind creating, standardizing, and optimizing our reporting infrastructure, specifically focusing on QlikSense. You will serve as the bridge between raw data and business strategy, ensuring that all entities and retail brands have access to accurate, unified, and actionable insights.
Beyond standard operations, you will lead the charge in data QA, investigative troubleshooting, and delivering tailored analytics solutions for special brand initiatives.
Key Responsibilities
1. Reporting Development & Governance
- QlikSense Administration: Own the creation, management, and optimization of reports and dashboards within QlikSense.
- Standardization: Establish and enforce a unified reporting standard across all organizational entities and retail brands to ensure one version of the truth.
- Ad-Hoc & Special Projects: Head the analytics group in gathering requirements, designing, and delivering custom reporting solutions for unique brand requests.
2. Data Quality & Troubleshooting
- Discrepancy Detection: Proactively audit reports to identify data anomalies, inconsistencies, or mismatches.
- Root Cause Analysis: Lead the technical team in diagnosing whether issues stem from data ingestion, ETL pipelines, or frontend logic.
- Documentation: Create detailed, structured outlines explaining data discrepancies and the corrective actions required.
3. Data Infrastructure Alignment
- Data Lake Integration: Collaborate with data engineering teams to understand the data lake architecture, ensuring seamless data flow from raw storage to reporting layers.
- Pipeline Optimization: Work closely with existing AWS Glue and Talend workflows to ensure data readiness for reporting.
Core Technical Skills
- BI & Visualization: Advanced proficiency in QlikSense (including Qlik Sense Cloud, Qlik Scripting, Set Analysis, and data modeling within Qlik). Experience with equivalent tools (Power BI, Tableau) will be considered if backed by strong data modeling fundamentals.
- Data Architecture: Solid understanding of Data Lake architecture (e.g., AWS S3, Delta Lake), including data tiering (raw, bronze, silver, gold zones), file formats (Parquet, CSV, JSON), and partitioning strategies.
- Data Querying & SQL: Expert-level SQL skills for querying complex databases, validating ETL outputs, and conducting deep-dive data reconciliation.
