Data Governance Lead

Roles & Responsibilities:-

1. Develop and maintain enterprise data governance strategies, policies, and standards aligned with business goals
2. Manage Microsoft Purview accounts, collections, and RBAC; optimize for large-scale environments (50TB+)
3. Design metadata repositories and maintain business glossaries and data dictionaries
4. Implement ingestion workflows via ADF, REST APIs, PowerShell, Azure Functions
5. Define classification rules and sensitivity labels (PII, PCI, PHI); integrate with MIP, DLP, Insider Risk Management
6. Define KPIs and dashboards to monitor data quality across domains
7. Maintain business glossary with domain owners and stewards; enforce approval workflows
8. Automate governance processes using PowerShell, Azure Functions, Logic Apps
9. Set up dashboards for audit logs, compliance reporting, and metadata coverage
10. Collaborate across business, IT, legal, and compliance teams for role alignment

Ideal Candidate:-

1. Strong Data Governance Lead Profile with deep Microsoft Purview and enterprise governance expertise
2. Mandatory (Experience 1): Must have 7+ years of experience in data governance and data management with at least the recent 3+ years of exposure in Microsoft Purview
3. Mandatory (Experience 2): Must have experience defining, leading, and operationalizing an enterprise data governance framework — strategy, policies, and standards aligned to business goals and regulatory compliance.
4. Mandatory (Tech skill 1): Must be proficient in Microsoft Purview — accounts, collections, RBAC, classification/labelling, DLP, Unified Catalog, Data Map, eDiscovery, Compliance Manager, and lifecycle/records management (ideally at large scale, 50TB+).
5. Mandatory (Tech skill 2): Must be strong in metadata management, lineage mapping (e.g. ADF → Synapse → Power BI), data classification, and security governance (PII/PCI/PHI, sensitivity labels, MIP/DLP)
6. Mandatory (Tech skill 3): Must have experience with data governance tools such as Informatica (Axon), Collibra, Atlan, or IBM IG Catalog.
7. Mandatory (Tech skill 4): Must have experience integrating governance with the Azure data stack (Data Lake, Synapse, SQL DB, Power BI) and Snowflake, and automating via ADF, REST APIs, PowerShell, Azure Functions / Logic Apps.
8. Mandatory (Tech skill 5): Must have experience defining data quality KPIs/dashboards, remediation workflows, and business glossary/stewardship (domain owners, approval workflows, metadata audits)
9. Mandatory (Compliance): Must have strong knowledge of GDPR, CCPA, HIPAA, and SOX compliance requirements.
10. Mandatory (Skill): Must be able to bridge technical governance with business and compliance goals, collaborating across business, IT, legal, and compliance teams, and driving governance training and change management
11. Mandatory (Company): B2B Tech/Consulting/B2B Software

IMMEDIATE JOINERS ONLY

Key Responsibilities:

1                   Data Flow Mapping & Lineage

2                   Design and produce end-to-end data flow maps for critical data items – from market source/core systems, through Data Lake staging, zone 2 and consumption zone 3, to downstream reporting, dashboards and analytics use cases.

3                   Document data lineage and transformations for critical data elements identified in the Master Data Requirements exercise, ensuring traceability that is audit-defensible and supports regulatory, governance and AI-readiness needs.

4                   Map data availability by country/Data Lake against the Product Hierarchy (to profit-centre level), building the consolidated Commercial Data Landscape view of what exists, where it sits (source only, staging, or consumption) and what is not captured at all.

5                   Document cross-system dependencies and cross-border data flows, flagging data residency, privacy and regulatory considerations to the appropriate governance forums.

6                   Data Inventory, Data Model Alignment & Gap Analysis

•                    Own and mature the data asset inventory, catalogue and data dictionary aligned to the consolidated Master Data Requirements Template – resolving “Not Specified”, “TBD” and “validation required” availability statuses through structured validation with Market IT and Data Engineering.

•                    Align business data requirements to the conceptual and logical data model of the target Data Lakehouse, ensuring the dependency between data model design and lineage tracking of critical data items is explicitly documented.

•                    Perform gap and hydration analysis by market and product, quantifying the scale of data that must be brought into the Data Lakes and providing sizing input to the Data Foundation Lead, DAE architects and Data Engineering delivery plans.

•                    Contribute to the Data Consolidation Plan towards a single source of truth, including recommended sequencing based on business priority (Top 200 fields, Horizon 1–3 priorities).

•                    Stakeholder Engagement & Workshops

•                    Work hand-in-hand with the Data Foundation Lead, Data & Analytics Programme Manager, Data Engineers, DAE/infrastructure architects, Local Market IT and Business stakeholders (Regional and Market) as the connective tissue between business data needs and technical implementation.

•                    Plan and facilitate structured interviews and cross-functional validation workshops with business, technology and data stakeholders to corroborate actual operating data flows against documented processes – establishing a fact-based, evidence-backed view rather than relying on documentation alone.

•                    Translate ambiguous business asks into clear data definitions, mapping rules and acceptance criteria that Data Engineering can build against, and play back findings to senior stakeholders in clear, visual, decision-oriented formats.

•                    Manage competing priorities across multiple markets with tact, keeping momentum in an environment of constrained capacity and differing local system landscapes.

•                    Governance, Ownership & Documentation

•                    Produce and maintain the Data Ownership & Accountability Matrix – mapping data owners, stewards and custodians per data asset and highlighting ownership gaps for remediation.

•                    Classify data assets by sensitivity and business function, aligning to Data Protection Policy, PDPA and applicable regulatory expectations (e.g. MAS guidelines) in partnership with Risk & Compliance.

•                    Ensure all data flow maps, lineage records and inventory artefacts are version-controlled, audit-ready and handed over cleanly into BAU data governance processes.

•                    Produce source to Datalake mapping document and data catalogue for missing data elements to complete Datalake hydration

Key Skills

•                    Data flow mapping and lineage documentation – proven ability to draw clear, accurate source-to-consumption data flow maps across complex multi-system, multi-market landscapes.

•                    Data architecture and modelling – strong grasp of conceptual/logical data models, data lake/lakehouse zone architectures (staging, conformed, consumption), data marts and MDM concepts.

•                    Data inventory, catalogue, dictionary and metadata management; familiarity with lineage/catalogue tooling (e.g. Purview, Collibra) and diagramming tools (e.g. Visio, ErWin, Lucidchart).

•                    Hands-on data profiling and validation skills (SQL essential) to verify what actually exists in source systems and lake zones.

•                    Strong stakeholder management – this is critical: able to build trust and drive outcomes across regional leadership, market business teams, Market IT and engineering, and to facilitate effective cross-functional workshops.

•                    Insurance domain knowledge – ideally commercial and specialty lines, with an understanding of policy, claims, reinsurance and underwriting data.

•                    Data quality, governance and privacy concepts, including cross-border data considerations.

•                    Clear written and visual communication – able to make complex data landscapes understandable to executives.

Experience & Qualification Requirements:

•                    8+ years’ experience in data architecture, data business analysis or information architecture roles, with at least:

•                    3+ years documenting data flows, lineage or data landscapes across multi-system / multi-market environments.

•                    Experience in Insurance or Financial Services, ideally within data foundation, data lake or data governance programmes.

•                    Demonstrated delivery of data inventory / data mapping / current-state data landscape assessments involving both business and IT stakeholders.

•                    Relevant certification (e.g. DAMA CDMP, TOGAF, cloud data platform certifications) preferred.

Mandatory Skills (Must Have)

• SQL and Database Design (5+ years in enterprise data platforms)

• Python development for automation and platform services (5+ years)

• Linux administration and Shell Scripting in production environments

• Metadata-driven systems and data management platform experience

• Technical leadership of offshore or distributed development teams Wissen Technology

• System analysis, troubleshooting, and production support expertise

• Metadata onboarding, integration, and governance processes

• Stakeholder management and delivery ownership across multiple teams

Key Responsibilities

• Lead developers supporting the Pantheon Metadata platform and related services

• Own end-to-end delivery of metadata onboarding and enhancement initiatives

• Design scalable metadata management solutions and automation capabilities

• Collaborate with architecture, infrastructure, and application teams for enterprise integration

• Participate in solution design reviews and establish development best practices

• Prioritize enhancements with business stakeholders and manage delivery commitments

• Provide production support, troubleshooting, and platform stability improvements Wissen Technology

• Drive modernization initiatives related to metadata lifecycle management

Technical Lead 

Job Description 

Role Overview 

We are seeking a highly skilled Data Engineering Lead with minimum of 5 years of hands-on experience in Azure data engineering, data warehousing, and automation-driven  integration. The ideal candidate should be a proven technical lead, capable of driving end-to-end 

project delivery and working closely with customer teams. This is a Work from Office / Customer Site role. 

Roles and Responsibilities 

• Lead the design, development, and delivery of data engineering and automation projects. 

• Architect and implement ETL/ELT pipelines using Azure Data Factory. 

• Design and manage enterprise data warehouses including dimensional modeling and 

schema optimization. 

• Manage Azure components including Storage Accounts, Data Lakes, Azure SQL, and Synapse. 

• Drive automation initiatives across data ingestion and transformation workflows. 

• Collaborate with business and technical stakeholders to translate requirements into scalable solutions. 

• Mentor team members and enforce engineering best practices. 

• Serve as the technical anchor responsible for ensuring high-quality, on-time delivery. 

Skills and Qualification 

• Strong hands-on experience with Azure Data Factory, Azure Storage, Azure SQL/Synapse. 

• Deep understanding of data warehousing concepts: star/snowflake schemas, fact/dimension modeling. 

• Experience with automation-led data engineering solutions. 

• Strong troubleshooting, optimization, and analytical skills. 

• Excellent communication and stakeholder management abilities. 

• Proven experience as a Technical Lead leading teams and delivery. 

Must Have 

• Min of 5 years of relevant data engineering experience. 

• Strong Azure Data Engineering and Data Warehousing expertise. 

• Proven Technical Lead experience. 

• Ability to work from office and customer site. 

• Strong ownership mindset with a focus on quality and delivery excellence.

Job Title: Senior Data Architect

Experience: 7+ Years

Location: India (Ahmedabad / Hybrid/Remote)

Job Summary

We are looking for an experienced Senior Data Architect to support the modernization and enhancement of Enterprise Analytic Data Warehouse (ADW). The candidate will be responsible for evaluating existing data architecture, designing scalable data models, assessing data integration across clinical, operational and financial domains, and establishing data governance and data quality standards.

Key Responsibilities

• Assess and review the existing Enterprise Analytic Data Warehouse architecture, database structures and data integration processes.
• Design and improve enterprise data models covering clinical, operational, financial and revenue-cycle information.
• Define data architecture standards, database structures, data relationships and integration strategies.
• Evaluate data flows between source systems and the data warehouse.
• Design and review source-to-target mappings and data lineage.
• Identify architectural gaps affecting data completeness, consistency, integrity and reporting accuracy.
• Establish standardized data definitions, data governance practices and metadata management.
• Ensure consistent patient, facility, payer and other critical identifiers across different datasets.
• Support integration of billing, claims, payment, denial and accounts receivable (AR) data.
• Work with SQL and Python-based data processing solutions to investigate and validate data structures.
• Recommend improvements to warehouse scalability, performance, reliability and maintainability.
• Develop a prioritized architecture and modernization roadmap for future implementation.
• Coordinate with data engineers, analytics engineers and business stakeholders.

Required Technical Skills

• Strong experience in Data Architecture and Enterprise Data Warehousing.
• Advanced SQL and relational database concepts.
• Experience with data modelling (conceptual, logical and physical).
• Knowledge of ETL/ELT pipelines and data integration.
• Experience with data profiling, data lineage and metadata management.
• Understanding of data governance, data quality and data validation frameworks.
• Experience working with large and complex datasets.
• Python knowledge for data analysis and validation.
• Experience with cloud-based data platforms or modern data warehouse technologies.

Preferred Skills

• Experience with Databricks, Azure, AWS or similar cloud data platforms.
• Experience with healthcare data warehouses.
• Knowledge of clinical, financial, billing and claims datasets.
• Understanding of HIPAA, PHI and healthcare data security principles.
• Experience with healthcare analytics, reimbursement and revenue-cycle data.

Role Overview

We are seeking an experienced Power BI & Microsoft Fabric Consultant to partner with our BI team for a short-term engagement focused on capability building, governance setup, and platform enablement.

The primary objective of this role is to upskill the team and establish a robust, scalable foundation for Power BI and Microsoft Fabric—covering security, governance, administration, and best practices—so that the team can independently manage and scale the platform post-engagement.

This is a hands-on + coaching role, not just advisory.

Key Responsibilities

1. Power BI & Fabric Enablement

• Conduct structured training sessions and workshops on:
• Power BI (Pro + Fabric-integrated experiences)
• Microsoft Fabric (Lakehouse, Warehouse, Dataflows Gen2, Pipelines, etc.)
• Build foundational understanding of:
• End-to-end analytics workflows in Fabric
• Integration between Power BI and Fabric workloads 
• Data Lake will be Snowflake

2. Governance & Security Framework

• Design and implement Power BI & Fabric governance model, including:
• Workspace strategy (Dev/Test/Prod separation)
• Naming conventions and standards
• Content lifecycle management
• Establish security architecture:
• Role-based access control (RBAC)
• Row-Level Security (RLS) / Object-Level Security (OLS)
• Data access patterns across Fabric and Power BI
• Define data sharing and access control processes

3. Administration & Platform Setup

• Configure and optimize:
• Power BI tenant settings
• Fabric capacity (capacity planning, workload management)
• Monitoring and usage metrics
• Set up:
• Deployment pipelines
• CI/CD best practices (where applicable)
• Audit logs and governance controls

4. Best Practices & Standards

• Define and document:
• Development standards (data modeling, DAX, report design)
• Performance optimization guidelines
• Dataset/reusable semantic model strategy
• Establish certification and promotion workflows for datasets and reports

5. Hands-On Implementation

• Work alongside the team to:
• Build or refactor key dashboards/reports using best practices
• Set up Fabric artifacts (Lakehouse/Warehouse/Pipelines)
• Ensure real use cases are implemented, not just theoretical training

6. Knowledge Transfer & Self-Sufficiency

• Provide:
• Playbooks, SOPs, and governance documentation
• Recorded sessions and training materials
• Mentor team members through:
• Office hours / Q&A sessions
• Code and architecture reviews
• Ensure the team can independently:
• Manage Fabric capacity
• Govern Power BI environment
• Implement secure and scalable solutions

Expected Outcomes (End of Engagement)

• Fully defined and implemented Power BI & Fabric governance framework
• Configured and optimized Fabric capacity + Power BI tenant
• Established security and access control processes
• Documented standards, playbooks, and operating model
• BI team capable of independent development, administration, and governance

Required Skills & Experience

Must-Have

• 5+ years of experience in Power BI development and administration
• Hands-on experience with Microsoft Fabric (end-to-end)
• Strong expertise in:
• Power BI governance and tenant administration
• Fabric capacity management
• Security models (RLS, RBAC, data access controls)
• Experience setting up enterprise BI governance frameworks
• Proven track record of training and mentoring teams

Good-to-Have

• Experience with data platforms (Snowflake, Azure, etc.)
• Knowledge of CI/CD for Power BI (DevOps integration)
• Familiarity with data catalog and lineage tools (e.g., Atlan, Alation)
• Understanding of modern data architecture patterns

Key Responsibilities

• Implement, configure, and support SAP Master Data Governance (MDG).
• Work on master data domains such as Business Partner, Customer, Supplier/Vendor, Material, and Finance.
• Configure change request types, workflows, validations, derivations, and business rules.
• Work with BRFplus, Floorplan Manager (FPM), and SAP MDG data models.
• Integrate SAP MDG with SAP S/4HANA and SAP ECC systems.
• Perform data replication using SAP DRF (Data Replication Framework).
• Troubleshoot data governance and replication issues.
• Prepare functional specifications and coordinate with ABAP developers.
• Support testing, data migration, deployment, and production incidents.

Required Technical Skills

• SAP MDG configuration and implementation.
• SAP MDG data models and change request management.
• Workflow configuration and approval processes.
• BRFplus, DRF, and SOA/ALE integration concepts.
• Knowledge of SAP S/4HANA and SAP Fiori.
• Understanding of master data quality, governance, and data migration.

Hiring: Data Modeler

Experience: 6+ years

Location: Bangalore/Gurugram/Pune

Required Skills

• Hands-on experience in conceptual, logical, and physical data modeling.
• Strong knowledge of ER modeling, dimensional modeling, star/snowflake schemas, fact/dimension tables, and SCDs.
• Strong SQL skills for data profiling, validation, and quality checks.
• Experience with source-to-target mapping, data dictionaries, metadata, and data lineage.
• Proficiency in Erwin, ER/Studio, or similar modeling tools.
• Exposure to Snowflake, Databricks, Redshift, SQL Server, or other data platforms.
• Understanding of data governance, UAT, reconciliation, and Agile methodologies.
• Strong analytical, documentation, and stakeholder management skills.

Preferred Qualifications

• Experience in enterprise data warehousing or lakehouse modeling.
• Knowledge of data products, data contracts, quality SLAs, and reusable datasets.
• Exposure to dbt, data cataloging, metadata management, and governance tools.
• Ability to work independently in a fast-paced environment.

If interested please share your cv at : snigdhaattheratebeanhr.com

Lead Data Engineer – Databricks & Azure

• We are looking for a Lead Data Engineer with 5–7 years of experience in Azure, Databricks, and Microsoft Fabric to lead and mentor the data engineering team. The role involves implementing scalable data solutions using Databricks as the core platform, building modern data pipelines, and enabling seamless integration with Microsoft Fabric and Power BI.

Responsibilities & Requirements:

• Lead and mentor data engineers while driving hands-on technical delivery.
• Implement approved Databricks and Microsoft Fabric architectures and solutions.
• Develop metadata-driven bronze, silver, and gold data pipelines.
• Build reusable batch, incremental, CDC, and streaming ingestion pipelines.
• Strong hands-on experience with Azure Databricks, PySpark, SQL, and Delta Lake.
• Experience with Microsoft Fabric, OneLake, Direct Lake, and Power BI integration.
• Implement data quality checks, reconciliation, source-to-target mapping, and metadata management.
• Configure Unity Catalog for data governance, access control, auditing, and lineage.
• Implement CI/CD, automated deployments, and Datadog monitoring.
• Drive testing, documentation, operational readiness, and knowledge transfer.

Good to have:

• Experience with MDM and master data integration.
• Exposure to Azure DevOps or GitHub Actions.
• Experience with automated data testing and reconciliation frameworks.
• Experience working with onshore stakeholders and distributed engineering teams.
• Strong communication, problem-solving, and team leadership skills.

1. Company Description:

Tatvic, is a marketing analytics company focusing on generating insights from data using long association with Google & its infrastructure. We breed,recognize and reward performance. As a company we are growing very fast & we are under transformation. To enable this transformation we need future leaders with eyesight which balances execution & strategic understanding.

Website: www.tatvic.com

2. Role Details

• Role: Lead Data Engineer
• Band: L4
• Department: Data Engineering
• Reporting to: L5 / Head of Delivery
• Location: Ahmedabad
• Job Type: Full Time
• Experience: 6-8 years of relevant experience
• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related technical field.

3. Mission & Role Overview

• Mission: To create high-level organizational impact by leading a team to design and deliver scalable, high-performance cloud-native data solutions. The Lead Data Engineer (L4) is responsible for ensuring technical excellence—specifically in BigQuery optimization and secure GCP/AWS architectures—while driving the transition to Agentic AI workflows and creating value through the development of L1-L3 engineers.
• Role Overview: This is a pivotal leadership role on the Management Track, responsible for end-to-end data outcomes for the function. The Lead Engineer ensures the quality of engineering execution, manages team utilization and resource allocation, and serves as the primary technical representative in cross-functional forums with Marketing, Strategy, and Product teams.

4. Key Responsibilities

• Lead the design, development, and deployment of scalable and efficient cloud-based data pipelines and architectures.
• Build reusable data solutions and software components that serve multiple customers and use cases.
• Architect cloud environments using GCP, ensuring best practices in IAM, Security, and Networking.
• Conduct Exploratory Data Analysis (EDA) on clickstream and behavioral datasets to derive insights.
• Manage end-to-end ETL processes, including data extraction, transformation, and loading using Python, SQL, and Airflow.
• Design and build production-grade software (APIs, services, internal tools) with sound engineering practices — version control, testing, CI/CD, and code review.
• Extend data platforms with AI capabilities — integrating LLMs, RAG pipelines, and agentic workflows where they add clear business value.
• Perform Root Cause Analysis (RCA) and resolve data quality, pipeline, or application issues.
• Collaborate cross-functionally with Marketing, Insights, Product, and Engineering teams to align data initiatives with business goals.
• Document architectures, design decisions, and operations clearly and consistently.
• Coach and mentor team members, ensuring technical excellence and continuous learning.
• Participate in recruitment and knowledge-sharing through interviews, blogs, and webinars.

5. Must-Have & Good-to-Have Skills

Must-Have Skills:

• Strong understanding of clickstream data and data pipeline architecture
• Hands-on experience with cloud platforms (GCP)
• Expertise in SQL, Python, and Airflow
• Solid software engineering foundation — API design, testing, version control, and deployment practices
• Proven ability to perform exploratory data analysis and data validation
• Knowledge of cloud security, IAM, and networking best practices
• Working knowledge of building or integrating AI/LLM-based components into existing systems
• Excellent problem-solving, stakeholder management, and communication skills

Good-to-Have Skills:

• Certifications such as: Google Cloud Professional Data Engineer / Cloud Architect
• AWS Certified Data Analytics or Azure Data Engineer Associate
• Google Analytics 4 (GAIQ)
• Experience in marketing attribution, commerce data, or analytics ecosystems
• Exposure to cost optimization and cloud resource management
• Experience with agent frameworks (LangChain, ADK, MCP) or LLM APIs
• Experience building automation frameworks or internal data tools

6. Not-a-Fit Criteria

• Individual Contributor Focus: Operates as the team's best individual contributor instead of building team capability or empowering L3s.
• Information Hoarding: Keeps architectural patterns or tribal knowledge trapped in their head rather than creating reusable playbooks and documentation.
• Avoidance of Conflict: Avoids difficult performance conversations with underperforming team members, allowing capability gaps to persist.
• Activity Over Outcomes: Measures success by "tasks completed" rather than pipeline stability, data accuracy, or business impact.
• Functional Siloing: Fails to build partnerships with Marketing or Product teams, leading to misaligned data delivery.

Our Culture & Perks - Where Passion Meets Purpose:

• Competitive CTC: Based on experience and skillset
• Medical Insurance for you and your family
• Return-to-Work Program for New Moms — because career breaks shouldn’t break momentum
• 1 Work From Home Day/Month — flexibility when life calls
• 2 Birthday Leaves/Year — one for you, one for someone you love
• Monthly Engagement Activities — from game nights to learning sessions
• Inclusive Culture & Equal Opportunity Employer — diversity drives our innovation
• We Think Like Engineers & Athletes: we build, we iterate, we scale, we win — together
• 5-day workweek — because balance drives brilliance