A road system with clearly marked, well-maintained roads and conscientious users who follow traffic rules results in a journey that’s smooth, predictable, and efficient. If traffic signals fail or roads are blocked without warning, confusion sets in, and delays and detours become the norm.

Now apply that logic to data engineering.

Today’s data teams are the backbone of digital transformation. They’re building the foundation that powers machine learning models, dashboards, and executive decisions. Imagine a leading bank working to combat financial fraud in real-time. The data engineering team is integrating transaction logs, customer behavior signals, third-party data, and geolocation inputs into a streaming pipeline that feeds risk detection models. The ability to respond quickly and adapt models on the fly can significantly improve fraud prevention outcomes and protect customer trust.

This is where Agile methodologies become indispensable. Agile provides the structured flexibility that modern data teams need to deliver fast, collaborate cross-functionally, and stay ahead of change. But principles alone are not enough. As we discussed in an "https://www.tigeranalytics.com/perspectives/blog/11-ways-data-engineers-can-leverage-an-agile-mindset-to-help-drive-value-efficiently-and-effectively/" target="_blank" rel="noopener noreferrer">earlier blog, success comes from pairing the right Agile mindset with a tailored framework like Tiger Gene. Together, they form the traffic rules that guide data engineering teams safely and swiftly through complexity, ensuring clarity, adaptability, and momentum in every sprint.

In this blog, we explore real-life data engineering projects we have worked on across a wide range of industries, from rapid pipeline deployments to large-scale platform builds. While the domain may change, the core delivery challenges often remain the same. That’s why we’ve applied a variety of Agile frameworks — such as Scrum and Kanban to Scaled Agile Framework (SAFe), Spotify Model, Large-Scale Scrum (LeSS), Disciplined Agile Delivery (DAD), Nexus, and Extreme Programming (XP), customized to address specific pain points around scale, speed, and coordination. We also share best practices to help teams apply these frameworks effectively in any domain.

1. Data Migration Projects: Managing complexity through iteration and feedback

Data migrations are among the most critical and high-impact initiatives. Migrations must preserve data quality, maintain lineage, ensure compliance, and minimize downtime, all while aligning with fast-changing business needs. Agile methodologies can ensure data migration projects occur seamlessly with minimal disruption.

• Project Example: Migrating an on-premises data warehouse to Snowflake
• Tech Stack: Snowflake, DBT, AWS Glue.
• Agile Methodology: Scrum
  Scrum’s iterative approach allows teams to break down the migration into manageable sprints, ensuring data accuracy and performance at each stage. Regular stakeholder reviews and adaptive planning mitigate risks associated with high-severity migrations.
• Scaled Approach: For large-scale data migrations involving multiple cross-functional teams, the Scaled Agile Framework (SAFe) offers structured coordination and governance. It supports alignment across teams through synchronized iterations (Program Increments), clear roles (like Release Train Engineer and Product Management), and system-level planning. The cadence-based planning and regular Inspect Adapt workshops help proactively identify dependencies, reduce bottlenecks, and manage risks early. Additionally, SAFe fosters continuous integration and system-level demos, ensuring end-to-end visibility and quality throughout the migration journey.
• Pain Point Addressed: Managing complexity and ensuring business continuity during high-risk migrations

Here is an in-depth look at how we at Tiger Analytics incorporate Agile principles for this project.

2. Real-Time Data Processing Projects: Structuring teams and workflows for low-latency demands

Real-time data processing projects, such as building recommendation engines, fraud detection systems, dynamic pricing models, and real-time customer engagement platforms, require data to be ingested, processed, and acted upon within milliseconds. These systems don’t just rely on accuracy, they also demand speed and high flexibility.

• Project Example: Real-time recommendation engine for e-commerce
• Tech Stack: Apache Kafka, Apache Flink, Elasticsearch
• Agile Methodology: Kanban
  Kanban’s flow-based structure allows teams to move through a defined series of stages or steps, visualized on a Kanban board. Each stage represents one part of the process (e.g., To Do, In Progress, Testing, Done), and the goal is to maintain a steady and efficient flow of work. By focusing on the movement of tasks rather than fixed iterations, teams can identify bottlenecks, limit work in progress (WIP), and ensure smooth, continuous delivery. This is especially important in environments like streaming data, where priorities may shift in real time.
• Scaled Approach: For organizations with multiple teams working on real-time data pipelines, the Spotify Model can foster autonomy and alignment through its squad-tribe-chapter-guild structure. Here,  each squad is a small team working on one feature (like a data pipeline), tribes are groups of squads working in the same area, while chapters connect people with the same role (like all data engineers), and guilds are communities of interest (like everyone passionate about real-time data).
• Pain Point Addressed: Handling rapidly changing requirements and ensuring low-latency data processing

3. Data Warehousing Projects: Aligning architecture with evolving analytical needs

Designing and implementing data warehouses requires meticulous planning and execution. The need is to align technical architecture with evolving business needs while ensuring scalability, data quality, performance, and governance. Teams must integrate disparate data sources, design efficient schemas, plan for historical and real-time data loads, and anticipate future use cases, without disrupting current operations.

• Project Example: Building a data warehouse for a financial institution
• Tech Stack: Amazon Redshift, Apache Hive, Snowflake
• Agile Methodology: Scrum
  Scrum’s sprint-based framework ensures clear objectives by breaking down complex tasks, like data modelling, ETL, and query optimization, into short, focused sprints.  Each sprint starts with sprint planning, where the team defines a clear goal and selects specific tasks with daily stand-ups to keep the team aligned. By the end of the sprint, a working piece (e.g., a data model or optimized query) is delivered, reviewed, and improved upon.  This cycle ensures that each stage delivers value with clear direction and measurable outcomes.
• Scaled Approach: For enterprise-level data warehousing projects, Large-Scale Scrum (LeSS) aligns multiple Scrum teams around a shared Product Backlog. This facilitates cross-team planning, and encourages continuous integration. It ensures that all teams contribute to building a unified, scalable data warehouse solution with minimal duplication and maximum collaboration.
• Pain Point Addressed: Managing large-scale data integration and ensuring alignment across teams

4. ETL Pipeline Optimization: Continuous refining for performance and reliability

Optimizing ETL pipelines is essential for improving performance and reducing processing times. As data volumes grow and sources diversify, the efficiency of ETL processes is directly tied to an organization’s ability to generate timely insights and support downstream applications.

• Project Example: Reducing ETL pipeline processing time by 30%
• Tech Stack: Apache Spark, Apache Airflow, Python
• Agile Methodology: Lean Agile
  Lean Agile’s focus on eliminating waste and improving efficiency aligns perfectly with pipeline optimization, ensuring faster and more reliable data processing. For example, removing redundant transformations and optimizing resource usage for faster, cost-effective ETL execution.
• Alternative Methodology: Extreme Programming (XP)
  XP’s emphasis on continuous integration and testing can help identify bottlenecks and improve pipeline performance iteratively. XP encourages frequent code commits, automated testing, and continuous integration, which helps detect issues, like slow transformations or data delays, faster. By running tests and deploying updates in small increments, teams can identify and fix bottlenecks early, leading to more reliable ETL performance.
• Pain Point Addressed: Reducing processing time and improving pipeline reliability

5. Data Quality Management Projects: Ensuring accuracy across distributed systems

Data quality is critical in industries like healthcare, where inaccuracies can have severe consequences. Ensuring clean, validated, and well-governed data across systems helps deliver accurate insights, supports clinical decision-making, and maintains compliance with health data regulations.

• Project Example: Implementing data quality checks for healthcare data
• Tech Stack: Talend Data Quality, Apache NiFi, SQL
• Agile Methodology: Scrum
  Scrum facilitates the incremental development of data quality checks and monitoring tools through structured sprints, enabling the team to prioritize critical data issues, incorporate stakeholder feedback regularly, and ensure timely compliance with healthcare regulations such as HIPAA and HL7.
• Alternative Methodology: Disciplined Agile Delivery (DAD)
  DAD incorporates specific process goals for risk management and governance, such as continuous verification, stakeholder involvement, and quality assurance. These help in enforcing data validation rules, audit trails, and compliance checks throughout the lifecycle, making it well-suited for healthcare and other regulated sectors.
• Pain Point Addressed: Ensuring data accuracy and compliance with regulatory standards

6. Big Data Analytics Projects: Managing complexity across ingestion, processing, and analysis

Big data projects involve processing massive datasets to derive actionable insights. These projects demand robust data architectures, distributed processing frameworks like Spark or Flink, and scalable storage solutions. Engineers must optimize for throughput, latency, and fault tolerance while also supporting analytics, machine learning pipelines, and business intelligence tools.

• Project Example: Analyzing social media data for customer sentiment analysis
• Tech Stack: Apache Hadoop, Apache Spark, Databricks
• Agile Methodology: Kanban
  Kanban’s continuous flow model is ideal for managing large datasets as it enables continuous delivery and real-time pipeline monitoring, which aligns with the iterative nature of big data workflows. It facilitates rapid feedback loops, minimizes batch delays, and enhances visibility into data processing stages, which are crucial for managing high-volume, low-latency systems.
• Scaled Approach: For enterprise-level big data projects, the Spotify Model can foster greater collaboration and innovation across autonomous teams. It organizes teams into autonomous squads aligned around specific features or data domains. Each squad owns a part of the big data pipeline, such as ingestion, processing, or analytics, and leverages DevOps practices for continuous delivery. Tribes and chapters ensure alignment, cross-pollination of technical expertise (e.g., in Spark optimization or data modelling), and shared architectural standards. This structure minimizes dependencies, accelerates experimentation, and supports scalable innovation across the entire data value chain.
• Pain Point Addressed: Managing large-scale data processing and ensuring timely insights

7. Streaming Data Integration Projects: Building resilient pipelines for real-time decisions

With the rise of IoT, integrating streaming data from devices and sensors is crucial for predictive analytics. Systems must be able to handle continuous data flows, filter noise, and maintain data integrity while supporting analytics models that detect anomalies, predict maintenance needs, or optimize operations on the fly.

• Project Example: Predictive maintenance in manufacturing using IoT data
• Tech Stack: Apache Kafka, Apache Flink, InfluxDB
• Agile Methodology: Scrum
  Scrum’s iterative approach allows data engineering teams to incrementally develop and optimize streaming data pipelines by delivering small, functional components in each sprint. For instance, one sprint may focus on configuring Kafka topics and producers to capture IoT sensor data, while the next may involve building Flink jobs to filter, aggregate, and detect anomalies in near real-time. Continuous integration and testing during each sprint ensure that data ingestion, transformation, and validation are aligned with business needs. Additionally, this approach supports rapid feedback loops from stakeholders, critical in refining data quality rules, windowing logic, or stateful stream processing. This agility helps maintain the integrity and reliability of real-time analytics systems while adapting to changes in data volume, schema evolution, or business priorities.
• Alternative Methodology: Nexus
  Nexus, a scaled Scrum framework, is ideal for coordinating multiple teams working on complex streaming data projects because it enables multiple Scrum teams to collaborate on complex, real-time data pipelines by ensuring continuous integration and coordination. In IoT-based predictive maintenance projects, teams work on Kafka ingestion, Flink stream processing, and InfluxDB storage in parallel. Nexus provides a shared Product Backlog, synchronized sprint events, and a Nexus Integration Team to manage inter-team dependencies, ensuring seamless real-time data integration, scalable architecture, and faster delivery of analytics capabilities.
• Pain Point Addressed: Ensuring real-time data integration and scalability

8. Data Governance and Compliance: Automating lineage, access, and audit across pipelines

As data regulations like GDPR and CCPA evolve, ensuring compliance is a top priority. Compliance requires complete visibility into how data is collected, processed, stored, and shared. For data engineers, this means implementing fine-grained access controls, establishing robust data lineage, ensuring encryption at rest and in transit, and integrating audit mechanisms across the data stack.

• Project Example: Establishing data governance policies for GDPR compliance
• Tech Stack: Collibra, Apache Atlas, AWS Glue
• Agile Methodology: Lean Agile
  Lean Agile’s focus on efficiency and alignment with legal requirements ensures that governance frameworks evolve with regulatory changes. This focus is achieved by implementing compliance features incrementally (e.g., encryption, audit logging), automating governance (using tools like Apache Atlas and Collibra), and collaborating closely with legal and security teams. The approach supports rapid adaptation to regulatory changes through feedback loops and continuous delivery. Technical practices include automated data lineage, policy-as-code, and reusable components like data masking and access control templates, ensuring scalable and up-to-date compliance.
• Alternative Methodology: Disciplined Agile Delivery (DAD)
  DAD’s governance and risk management capabilities include structured decision-making frameworks, role-based accountability, and built-in compliance checkpoints. This ensures that data policies are consistently enforced across pipelines, metadata is properly cataloged (using tools like Apache Atlas), and sensitive data is flagged and handled appropriately. DAD also supports risk identification and mitigation early in the lifecycle through continuous feedback loops, traceability mechanisms, and integration with governance platforms like Collibra. This enables proactive compliance with standards like GDPR/CCPA while minimizing disruptions to delivery.
• Pain Point Addressed: Aligning data governance with evolving regulatory requirements.

9. Data Lake Implementation Projects: Designing flexible architectures for diverse data at scale

Data lakes serve as centralized repositories for structured and unstructured data, requiring adaptability to evolving business needs. Unlike data warehouses that prioritize structure and strict schemas, data lakes embrace a schema-on-read approach, allowing teams to store now and analyze later. The lack of upfront structure requires constant curation, governance, and adaptability.

• Project Example: Building a data lake for a retail company
• Tech Stack: Amazon S3, Apache Hadoop, Delta Lake
• Agile Methodology: Kanban
  Kanban’s operational flexibility — particularly its ability to visualize workflow, limit work in progress (WIP), and support continuous delivery — enables data engineering teams to iteratively refine data ingestion pipelines and retrieval processes, ensuring the data lake architecture remains both scalable and responsive to changing business needs.
• Scaled Approach: For large-scale data lake projects, SAFe can provide a structured approach to align teams and deliver value incrementally. The approach involves four stages – Program Increment planning to align team objectives and dependencies across ingestion, storage, and analytics, Agile Release Trains to ensure synchronized delivery of features like data schema evolution, metadata management, and pipeline automation, System Demos and Inspect Adapt cycles for continuous feedback and improvement across all technical layers, and Built-in Quality practices to enforce governance, data validation, and security standards at scale.
• Pain Point Addressed: Managing scalability and evolving business requirements

10. Data Visualization Projects: Enabling clear, actionable insights through close collaboration and iteration

Data visualization projects require close collaboration with stakeholders to deliver actionable insights. Engineers translate complex data into meaningful visuals that align closely with end users, from marketing and finance to operations and the C-suite, fuelling smarter decisions.

• Project Example: Developing interactive dashboards for sales performance
• Tech Stack: Tableau, Power BI, D3.js.
• Agile Methodology: Scrum
  Scrum’s iterative feedback loops involve regular reviews and stakeholder demos at the end of each sprint, allowing for real-time course correction and ensuring that visualizations align with stakeholder expectations and deliver value at each sprint. It reduces the risk of misinterpretation and rework, keeping development tightly coupled with stakeholder expectations.
• Alternative Methodology: Extreme Programming (XP)
  While Scrum emphasizes iterative feedback at the end of each sprint, XP goes a step further by encouraging continuous feedback through practices like pair programming, frequent releases, and on-site customer involvement. XP promotes real-time adjustments during development, not just at sprint boundaries, making it more granular and developer-integrated.
• Pain Point Addressed: Aligning visualizations with stakeholder expectations

11. Machine Learning Pipeline Projects: Improving model accuracy through feedback and continuous testing

Machine learning projects, such as fraud detection, require frequent adjustments based on model performance, such as retraining with new data, and close monitoring of key metrics like precision, recall, and false positive rates.

• Project Example: Fraud detection in financial transactions
• Tech Stack: TensorFlow, PyTorch, MLflow.
• Agile Methodology: Kanban or Lean Agile
  Both methodologies provide the flexibility to adjust model parameters, retrain models, and incorporate real-time feedback.
• Alternative Methodology: Extreme Programming (XP)
  XP’s focus on continuous testing and integration ensures that machine learning models are robust and accurate.
• Pain Point Addressed: Ensuring model accuracy and adaptability

12. Data Cataloguing and Metadata Management: Maintaining data lineage and governance through incremental delivery

Tracking data lineage and metadata is essential for ensuring transparency and efficiency in large organizations. Strong lineage and metadata practices help organizations avoid risk compliance violations, data integrity issues, and reduced trust in analytics.

• Project Example: Implementing a metadata catalogue for data lineage
• Tech Stack: Apache Atlas, Collibra, Alation.
• Agile Methodology: Kanban
  Kanban’s flow-based approach ensures smooth updates and additions to metadata catalogues by visualizing work in progress through boards, limiting WIP (Work In Progress) to prevent overload, and using continuous delivery practices. This allows metadata entries (like data assets, lineage paths, or glossary terms) to be incrementally added, reviewed, and validated in small batches, ensuring consistent governance without bottlenecks. Integration with tools like Apache Atlas or Collibra supports automated lineage tracking, minimizing manual effort and errors.
• Alternative Methodology: Disciplined Agile Delivery (DAD)
  DAD ensures that metadata management practices are well-defined and adhere to organizational policies by focusing on:

   

  • Governance: Implementing clear policies and controls to track and validate data lineage, ensuring the integrity and consistency of metadata across systems.
  • Risk Management: Regularly assessing risks such as data breaches, non-compliance with regulations (e.g., GDPR), and potential inaccuracies in analytics by using automated checks, audits, and compliance reporting mechanisms.
• Pain Point Addressed: Ensuring data transparency and governance

What it takes to build an integrated agile practice in data engineering

As data systems grow in complexity, agile data engineering is also evolving beyond foundational principles to embrace a more holistic, technology-forward approach that meets the demand for high-quality and trustworthy data. Through our work and conversations with data leaders, we’ve observed businesses exploring AI-powered tools for sprint planning, backlog management, and performance tracking. There is also a clear shift towards hybrid agile frameworks, drawing from the best of Scrum, Kanban, SAFe, Spotify Model, LeSS, DAD, Nexus, and XP, and tailoring these methodologies to specific industry and project needs. At the same time, embedding security and compliance into Agile workflows is becoming a priority as regulatory expectations grow. Businesses are also integrating DevOps with Agile practices to automate workflows and improve cross-functional collaboration, and applying Agile principles when adopting DataOps to ensure data reliability and speed.

Whether it’s real-time data processing, machine learning pipelines, or data governance, Agile, when combined with the right tools and frameworks, gives data engineering teams the adaptability and structure needed to build scalable, reliable, and value-driven solutions in today’s dynamic landscape.