Business Analyst (Data Analyst) - Enterprise Datalakes Implementation Project

About the Role

We are actively seeking a highly skilled, results-oriented Contract Business Analyst / Data Analyst with deep functional and technical expertise in large-scale Enterprise Data Lake Implementations. In this role, you will bridge the gap between complex enterprise business requirements and modern cloud data engineering architectures.

The successful candidate will serve as the analytical nexus within a multi-phased modernization program aimed at centralizing enterprise data into a scalable Microsoft Azure Data Lake ecosystem. We require a specialized professional with between 5 and 15 years of progressive experience who excels at data profiling, metadata management, source-to-target mapping, business process re-engineering, and validation of distributed data pipelines. You will collaborate closely with enterprise business stakeholders, data engineers, enterprise architects, and analytics teams to turn raw business inputs into robust, high-performance data lake products.

Key Objectives & Project Scope

This initiative involves migrating disparate legacy operational databases, ERPs, CRM systems, and external third-party feeds into a modern, centralized Azure Data Lake architecture. Core project goals include:

• Decommissioning legacy siloed data marts and transitioning workloads to a unified Azure Data Lake Storage (ADLS Gen2) environment.
• Establishing standardized Bronze (Raw), Silver (Curated/Cleansed), and Gold (Business Aggregates) layers to serve downstream BI and Advanced Analytics use cases.
• Enforcing strict data governance, lineage tracking, and data quality frameworks across complex transactional and analytical pipelines.
• Accelerating data democratization by providing business units with self-service analytics models supported by optimized Azure Synapse and Power BI assets.

Detailed Roles & Responsibilities

1. Business & Technical Requirements Engineering

• Lead discovery sessions, workshops, and structured interviews with enterprise cross-functional stakeholders to capture data demands, consumption patterns, and analytical needs.
• Translate ambiguous, high-level business requirements into precise technical epics, user stories, and acceptance criteria tailored for an Agile data platform delivery team.
• Construct end-to-end functional requirement documents (FRDs), technical design specifications, and business requirement documents (BRDs).
• Define and document granular source-to-target mapping (STTM) matrices detailing ingestion frequency, transformation rules, filtering criteria, and business logic.

2. Advanced Data Analysis, Profiling & Modeling

• Perform deep-dive data profiling on diverse data sources (RDBMS, NoSQL, Semi-structured JSON/XML, flat files) to detect data anomalies, schema drift, nullability patterns, and cardinality issues.
• Write complex SQL queries, analytical functions, and stored procedures across multi-terabyte data stores to validate historical trends and edge cases.
• Partner with Data Architects to design multidimensional schemas (Star, Snowflake) and logical/physical data models for the Gold/Consumption layers within the Azure ecosystem.
• Establish and document clear definitions for Enterprise Key Performance Indicators (KPIs), metrics, and attributes to drive a unified business semantic layer.

3. Azure Data Lake Ecosystem Collaboration

• Work in lockstep with Data Engineers leveraging Azure Data Factory (ADF), Azure Databricks, and Azure Synapse to ensure data ingestion pipelines match business rules exactly.
• Participate in defining and evaluating data lake partitioning strategies, file formats (Parquet, Delta Lake), and lifecycle management policies to balance performance with cost optimization.
• Support metadata management and data cataloging processes using platforms like Microsoft Purview, ensuring data lineage is continuously tracked and maintained.

4. Data Governance, Quality & Compliance

• Formulate comprehensive Data Quality (DQ) metrics, rules, and threshold frameworks; orchestrate automated data profiling and exception handling processes.
• Ensure compliance with enterprise data privacy mandates, enterprise access governance, and regulatory standards (e.g., GDPR, CCPA, HIPAA, SOC2) during data ingestion and curation.
• Identify data ownership models and partner with designated Data Stewards to resolve structural and semantic discrepancies across source systems.

5. Quality Assurance, User Acceptance Testing (UAT) & Deployment

• Author and execute rigorous test scripts, validation queries, and acceptance test plans to reconcile source data against target lakehouse layers.
• Coordinate and facilitate formal User Acceptance Testing (UAT) cycles with enterprise business users, logging defects, orchestrating triage sessions, and managing backlogs to resolution.
• Develop training materials, data dictionaries, and operational runbooks; conduct knowledge-transfer sessions to accelerate user adoption of modern data assets.

Required Qualifications & Technical Expertise

• Work Experience: 5 to 15 years of demonstrable experience as a Business Analyst, Data Analyst, or Systems Analyst, specifically within enterprise data warehousing and big data initiatives.
• Data Lake Experience: Direct involvement in at least one full-lifecycle Enterprise Data Lake / Lakehouse design, development, or migration initiative.
• Microsoft Azure Ecosystem: Comprehensive, hands-on functional and technical understanding of:
• Advanced Querying & Data Manipulation: Expert-level SQL proficiency (complex joins, window functions, recursive queries, query optimization).
• Source-to-Target Mapping (STTM): Exceptional ability to generate exhaustive STTM documentation capturing transformations, joins, aggregate logic, and metadata.
• Data Modeling: Strong understanding of relational data modeling, Data Vault 2.0, dimensional modeling (Kimball methodology), and medallion (Bronze/Silver/Gold) architectures.
• Business Intelligence (BI): Familiarity with reporting tools, especially Power BI (DAX, Power Query), to prototype dashboards and validate data pipelines visually.
• Agile Methodologies: Deep expertise operating inside Agile, Scrum, or Scaled Agile (SAFe) software development lifecycles utilizing Jira, Azure DevOps, or Confluence.
• Education: Bachelor's or Master's degree in Computer Science, Information Systems, Business Analytics, Data Management, or a related discipline.

Preferred / Nice-to-Have Skills

• Relevant Industry Certifications:
  • Microsoft Certified: Azure Data Fundamentals (DP-900)
  • Microsoft Certified: Azure Data Engineer Associate (DP-203)
  • Microsoft Certified: Power BI Data Analyst Associate (PL-300)
  • Certified Business Analysis Professional (CBAP) or PMI-PBA
• Working knowledge of scripting languages (Python, PySpark, or R) for advanced data exploration and validation.
• Experience handling change management and data governance frameworks in regulated sectors (Finance, Healthcare, Insurance, or Retail).

Soft Skills & Professional Attributes

• Exceptional Communication: Articulate complex architectural and analytical concepts to non-technical business partners, executive leadership, and deep technical engineers alike.
• Analytical Mindset: High attention to detail with an investigative approach to data discrepancies and missing business logic.
• Stakeholder Negotiation: Proven track record of managing competing priorities, scoping constraints, and dynamic requirements in enterprise environments.
• Autonomous Driver: Self-starter capable of navigating ambiguity, establishing alignment, and executing tasks on schedule within matrixed contract environments.

Why Join This Initiative?

This project represents a critical strategic investment in modern data architecture. You will be placed at the core of a high-visibility, mission-critical transformation where your contributions directly shape how the enterprise governs, surfaces, and consumes analytical data for years to come. We offer a competitive rate structure, exposure to top-tier enterprise cloud architectures, and a collaborative team of experienced data practitioners.