Data Governance Lead (Microsoft Purview)

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

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

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.

Job Description: Associate Director-Data Engineering (Databricks Lakehouse Architect + Cloud Data Engineering Leader)

About our Client -

Client is a leading SAP consulting and digital transformation partner committed to delivering innovative, high-impact solutions across enterprise analytics, cloud, and intelligent business platforms.

As enterprises modernize their data landscapes, the role of traditional data warehousing is rapidly evolving into scalable cloud-native Data Lakehouse architectures. Applexus is expanding its modern data engineering capabilities to help customers integrate enterprise data from SAP and non-SAP ecosystems, build advanced analytics platforms, and enable future-ready AI-driven solutions.

We are looking for an accomplished Databricks Lakehouse Architect / Cloud Data Engineering Leader who can play a key role in this transformation journey — designing modern data platforms, leading cloud data engineering initiatives, and bridging enterprise analytics with next-generation technologies.

The ideal candidate will combine strong hands-on expertise in Databricks, Spark/PySpark, Delta Lake, and Azure/AWS cloud platforms with the ability to engage customers, architect solutions, guide engineering teams, and drive successful delivery.

Role Overview:

We are looking for a hands-on Data Engineering leader who can architect modern cloud data platforms using Databricks Lakehouse and lead solution design, delivery, and engineering teams. This role requires strong technical ownership rather than a pure people management background.

Key Responsibilities:

• Architect scalable Data Lakehouse platforms

• Own solution design, estimation, architecture reviews and client discussions

• Build and optimize enterprise data pipelines

• Provide technical guidance to data engineering teams

• Establish best practices around performance, governance and architecture

Must Have - Databricks Architecture:

• Databricks Lakehouse design

• Delta Lake implementation

• Apache Spark / PySpark engineering

• Medallion architecture (Bronze/Silver/Gold layers)

• Data pipeline optimization

• Unity Catalog and data governance exposure

Cloud Platform Expertise (depth in one required):

Microsoft Azure:

• Azure Databricks

• Azure Data Factory (ADF)

• Azure Data Lake Storage (ADLS)

• Azure Synapse Analytics

OR

Amazon AWS:

• Databricks on AWS

• Amazon S3

• AWS Glue

• Redshift

• Athena

Good to Have:

• Snowflake

• ML Engineering exposure

• Cloud/Data Engineering certifications

Ideal Candidate:

A hands-on architect who can talk to customers, design solutions, estimate projects, guide engineers and review architecture while helping build a modern Data Engineering practice.

 We are looking for an accomplished Databricks Lakehouse Architect / Cloud Data Engineering Leader who can play a key role in this transformation journey — designing modern data platforms, leading cloud data engineering initiatives, and bridging enterprise analytics with next-generation technologies.

The ideal candidate will combine strong hands-on expertise in Databricks, Spark/PySpark, Delta Lake, and Azure/AWS cloud platforms with the ability to engage customers, architect solutions, guide engineering teams, and drive successful delivery.

If you are a hands-on Data Architect who enjoys building modern data platforms and wants to move beyond execution into solution ownership and leadership, I would love to connect.

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.

Position: Technical Architect – Data Engineering

Job Summary:

• We are looking for an experienced Technical Architect to lead the design and implementation of modern cloud-based data platforms.
• The ideal candidate should have strong expertise in Azure and/or AWS, Databricks, Snowflake, modern data architecture, and large-scale data engineering.
• The candidate will work closely with business stakeholders, architects, and engineering teams to deliver scalable, secure, and high-performance data solutions.

Key Responsibilities

• Design enterprise-scale data lakehouse and data warehouse architectures.

• Define data ingestion, transformation, and serving architecture.

• Lead architecture discussions and technical governance.

• Design scalable ETL/ELT frameworks using Databricks and Snowflake.

• Define best practices for security, performance, CI/CD, and DevOps.

• Guide engineering teams on implementation.

• Collaborate with business, product, and data governance teams.

Must Have Skills :

• Azure or AWS

• Databricks

• Snowflake

• Data Lakehouse Architecture

• PySpark

• SQL

• Data Modelling

• ETL/ELT Architecture

• Performance Optimization

• CI/CD

• Terraform or Infrastructure as Code (preferred)

Preferred

• Insurance domain

• dbt

• Unity Catalog

• Delta Lake

• Iceberg

• Azure Data Factory / AWS Glue  

Sr. Data Engineer 

Location: Pune

Responsibilities 

· Lead the end-to-end design and development of scalable, high-performance data pipelines and ETL/ELT frameworks aligned with modern data engineering best practices. 

· Architect complex data integration workflows that bring together structured, semi-structured, and unstructured data from both cloud and on-premise sources. 

· Build robust real-time, batch, and on-demand pipelines with built-in observability—monitoring, alerting, and automated error handling. 

· Partner with analysts, data scientists, and business leaders to define and deliver reliable data models, quality frameworks, and SLAs that power key business insights. 

· Ensure optimal pipeline performance and throughput, with clearly defined SLAs and proactive alerting for data delivery or quality issues. 

· Collaborate with platform, DevOps, and architecture teams to build secure, reusable, and CI/CD-enabled data workflows that align with enterprise architecture standards. 

· Establish and enforce the best practices in source control, code reviews, testing automation, and continuous delivery for all data engineering components. 

· Lead root cause analysis (RCA) and preventive maintenance for critical data failures, ensuring minimal business impact and continuous service improvement. 

· Guide the team in establishing standards for data modeling, transformation logic, and governance, ensuring long-term maintainability and scalability. 

· Design and execute comprehensive testing strategies—unit, integration, and system testing—ensuring high data reliability and pipeline resilience. 

· Monitor and fine-tune data pipeline and query performance, optimizing for reliability, scalability, and cost-efficiency. 

· Create and maintain detailed technical documentation, including data architecture diagrams, process flows, and integration specifications for internal and external stakeholders. 

· Facilitate and lead discussions with business and operational teams to understand data requirements, prioritize initiatives, and drive data strategy forward. 

Qualifications 

· 7 to 10 years of hands-on experience in data engineering roles with a proven record of building scalable and secure data platforms. 

· Over 5 years of experience in scripting languages such as Python for data processing, automation, and ETL development. 

· 4+ years of experience with Snowflake, including performance tuning, security model design, and advanced SQL development. 

· 5+ years of experience with data integration tools such as Azure Data Factory, Fivetran, or Matillion. 

· 5+ years of experience in writing complex, highly optimized SQL queries on large datasets. 

· Proven experience integrating and managing APIs, JSON, XML, and webhooks for data acquisition. 

· Hands-on experience with cloud platforms (Azure/AWS) and orchestration tools like Apache Airflow or equivalent. 

· Experience with CI/CD pipelines, automated testing, and code versioning tools (e.g., Git). 

· Familiarity with dbt or similar transformation tools and best practices for modular transformation development. 

· Exposure to data visualization tools like Power BI for supporting downstream analytics is a plus. 

· Strong interpersonal and communication skills with the ability to lead discussions with technical and business stakeholders. 

Education 

Bachelor’s or Master’s degree in Computer Science/Information technology or related field. 

Equivalent academic and work experience can be considered. 

Role Overview

We are seeking a Senior Reporting Analyst / Business Intelligence Lead to own and drive enterprise reporting, analytics, and data insights across the organization. This is a senior, hands-on role requiring deep technical expertise across the Microsoft data and BI stack, strong business acumen, and the ability to operate with minimal hand-holding. The ideal candidate will act as a bridge between business leadership and technical teams, translating complex business questions into scalable, reliable data models and executive-ready insights.

Key Responsibilities

·       Serve as the primary owner of enterprise reporting and BI solutions, from data ingestion through executive dashboards.

·       Design, build, and maintain scalable Power BI / Microsoft Fabric semantic models using STAR schema best practices.

·       Partner closely with business stakeholders and C-level executives to define KPIs, metrics, and reporting requirements.

·       Lead and present weekly reporting and insights sessions with senior and executive leadership.

·       Ensure data accuracy, consistency, and trust across all reports and analytics outputs.

·       Proactively identify data quality issues, root causes, and remediation strategies.

·       Drive automation of reporting, alerts, and data refresh processes to reduce manual effort.

·       Collaborate with engineering, data, and application teams to align business logic with source system data structures.

·       Mentor junior analysts and establish BI standards, best practices, and documentation.

Technical Skills – Data & Backend

·       Advanced SQL: complex queries, views, stored procedures, and validation against source systems (RDN, Recovery Connect).

·       Azure Data Factory: build and manage copy pipelines from SQL sources to data warehouses / dedicated pools.

·       Incremental refresh, parameterized pipelines, and schema replication.

·       Data warehouse design: STAR schema, fact vs dimension tables, handling deleted records and SCDs.

Technical Skills – Reporting & Frontend

·       Power BI / Microsoft Fabric: Power Query, DAX, and M language expertise.

·       Semantic model design (1-to-many, 1-to-1, many-to-1 relationships).

·       Query folding and performance optimization best practices.

·       Paginated Report Builder (SSRS-style) for automated Excel and PDF reporting.

·       Power Automate integration for alerts and workflow-driven notifications.

Data Quality & Analytics

·       Ability to clean, normalize, and reconcile data across multiple systems.

·       Strong troubleshooting skills when business definitions and data do not align.

·       Translate business terminology into accurate technical mappings and calculations.

Communication & Leadership

·       Serve as a key liaison between business stakeholders and technical teams.

·       Comfortable working directly with C-level executives and senior leadership.

·       Ability to lead executive-level meetings and clearly communicate insights.

·       Self-starter mindset with minimal hand-holding.

Qualifications

·       7–10+ years of experience in BI, analytics, or enterprise reporting roles.

·       Preferred experience in financial services or regulated environments.

·       Strong experience across the Microsoft BI ecosystem.

Preferred Certifications

·       Microsoft PL-300 (Power BI Data Analyst)

·       Microsoft DP-600 (Fabric Analytics Engineer)

·       Microsoft DP-700 (Fabric Data Engineer)

What Success Looks Like

·       Executive leadership trusts and relies on BI dashboards for decisions.

·       Reporting is automated, accurate, and scalable.

·       BI functions as a strategic partner to the business.

Job Description:

We are seeking a skilled Senior Data Engineer with expertise in Databricks to join our dynamic data team. The ideal candidate will design, build, and maintain scalable data pipelines and architectures to support our organization's data-driven initiatives and leverage Databricks to process large-scale datasets, optimize data workflows, enable advanced analytics and machine learning, and integrate Power BI for data visualization and reporting.

Key Responsibilities

·        5-8 years of professional work experience in a relevant field

·        Proficient in Microsoft Fabric platform, Azure Databricks, ADF, Delta Lake, SQL Data Warehouse, Unity Catalog.

·        Good Experience on Microsoft Dynamics 365

·        Experience/ prior knowledge on semi structure data and Structured Streaming, Azure synapse, data lake, data warehouse.

·        Proficient in creating Azure Data Factory pipelines for ETL/ELT processing; copy activity, custom Azure development etc.

·        Good knowledge of SQL and Python for data manipulation, transformation, and analysis

·        Understand business requirements to set functional specifications for reporting applications

• Data Pipeline Development: Design, develop, and maintain robust, scalable data pipelines using Databricks, Apache Spark, and other cloud-based technologies.
• Data Integration: Ingest, transform, and integrate data from diverse sources, including APIs, databases, streaming platforms, and third-party systems, into Databricks for analytics and reporting.
• Power BI Integration: Develop and optimize data models and datasets in Databricks for use in Power BI, ensuring efficient data connections and high-quality visualizations.
• Performance Optimization: Optimize data workflows, API calls, and queries on Databricks and Power BI for performance, cost-efficiency, and scalability.
• Data Modeling: Build and maintain data models to support business requirements, ensuring data quality, consistency, and accessibility for analytics and reporting.
• Cloud Integration: Implement data solutions on cloud platforms (e.g., AWS, Azure, GCP) integrated with Databricks, Power BI, and API ecosystems.
• Security & Compliance: Implement data governance, security, and compliance best practices within Databricks, Power BI, and API environments.
• Technical Skills:
• Proficiency in Databricks, including Delta Lake, Spark SQL.
• Strong programming skills in Python, Scala, or Java.
• Experience with Apache Spark for big data processing.
• Knowledge of SQL for querying and transforming data.
• Proficiency in Power BI for creating data models, DAX queries, and interactive dashboards.

Preferred Qualifications

• Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional).

Experience with real-time data processing and streaming

Tech Lead – Data Lake Platform

Recruitment & Payroll Company: Neogencode Technologies

Job Location: Sector 43, Gurugram, Haryana

Work Mode: On-site (Work from Office)

Working Days: 5 days a week

Experience Required: 5+ years

Employment Type: Full-time

About the Opportunity

We are hiring a Tech Lead – Data Lake Platform. This opportunity involves working on an enterprise-grade AWS data lake platform supporting data engineering, analytics, reporting, and business applications.

The candidate will be employed on the payroll of Neogencode Technologies and will work at the client location in Sector 43, Gurugram.

Key Responsibilities

• Own and design end-to-end data lake architecture, including data ingestion, staging, curated layers, and data consumption.
• Lead data engineering pipelines using AWS services, Apache Airflow, Spark/PySpark, and dbt.
• Manage AWS data services such as S3, EMR, Glue, Athena, Redshift, Lambda, RDS, IAM, and DMS.
• Design and support API/data-serving solutions using PostgreSQL, Hasura GraphQL, or similar technologies.
• Improve pipeline performance, platform reliability, monitoring, and service-level agreements (SLAs).
• Ensure data security, access control, personally identifiable information (PII) protection, and governance.
• Mentor data engineers, conduct code reviews, and handle production incidents and root-cause analysis.
• Collaborate with business and application teams to deliver data and reporting solutions.

Required Skills

• 5+ years of experience in data engineering or platform engineering, including technical leadership.
• Strong hands-on experience with AWS data services and enterprise data lake architecture.
• Production experience with Apache Airflow, Spark/PySpark, and SQL.
• Strong understanding of data modeling and layered data architecture.
• Experience with PostgreSQL and Hasura GraphQL or a similar API/data-serving layer.
• Proven experience in technical leadership, production support, and troubleshooting.

Good to Have

• Experience with dbt and lakehouse table formats.
• Knowledge of Kafka or streaming data ingestion.
• Experience with Redshift optimization and workload design.