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FIN9698 Mastering MiFID II for DataOps Engineers in Financial Services

$199.00
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What is the MiFID II for DataOps Engineers course about?

DataOps teams in financial services often rebuild similar compliance pipelines from scratch each reporting cycle. Without standardised, documented patterns, effort doesn’t compound, it repeats. This drains bandwidth, delays audit readiness, and limits visibility into data lineage when regulators dig deeper.

What situation is the MiFID II for DataOps Engineers for?

DataOps teams in financial services often rebuild similar compliance pipelines from scratch each reporting cycle. Without standardised, documented patterns, effort doesn’t compound, it repeats. This drains bandwidth, delays audit readiness, and limits visibility into data lineage when regulators dig deeper.

Who is the MiFID II for DataOps Engineers course for?

DataOps Engineer at a global financial institution, responsible for building and maintaining data pipelines that feed regulatory reports under MiFID II, with a focus on traceability, schema consistency, and audit resilience.

Who is the MiFID II for DataOps Engineers course not for?

Engineers focused solely on non-regulated data pipelines or those outside financial services with no MiFID II or similar transparency mandate.

What do you take away from the MiFID II for DataOps Engineers course?

A documented, version-controlled library of reusable pipeline patterns for MiFID II data flows Faster audit onboarding due to self-documenting data lineage and metadata tagging Cross-functional teams adopting your patterns as the default for compliance-adjacent work Reduced rework when control expectations shift or new instruments are added to scope Proven approach to turn one-off data fixes into long-term, repeatable control assets.

How does this map to your situation?

MiFID II pipeline design and audit readiness Data lineage and cross-system consistency Regulatory inquiry response and collaboration Scaling and sustaining compliance practices.

What's included with your purchase?

12 modules with 12 chapters each (144 chapters) Downloadable templates and worked examples for every module Hand-built implementation playbook delivered alongside course access 30-day money-back guarantee.

What does the MiFID II for DataOps Engineers cover on delivery and format?

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access. Time investment: Approximately 2.5 hours per module, designed for integration into active project work , apply each lesson directly to current pipelines.

Closely related courses: MiFID II for Financial Compliance Practitioners, MiFID II for Financial Services Associates, MiFID II for Financial Data Leaders, MiFID II for Financial Conduct Specialists.

More answers: what you get with every course, refund policy, all help answers.

A tailored course, built for your situation

Mastering MiFID II for DataOps Engineers in Financial Services

Build a compounding library of compliant, reusable data pipelines that accelerate audit readiness and cross-functional trust

$199 one-time
24-hour access provisioning 30-day money-back guarantee Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Spending cycles rebuilding similar data validation pipelines instead of advancing strategic initiatives

The situation this course is for

DataOps teams in financial services often rebuild similar compliance pipelines from scratch each reporting cycle. Without standardised, documented patterns, effort doesn’t compound, it repeats. This drains bandwidth, delays audit readiness, and limits visibility into data lineage when regulators dig deeper.

Who this is for

DataOps Engineer at a global financial institution, responsible for building and maintaining data pipelines that feed regulatory reports under MiFID II, with a focus on traceability, schema consistency, and audit resilience

Who this is not for

Engineers focused solely on non-regulated data pipelines or those outside financial services with no MiFID II or similar transparency mandate

What you walk away with

  • A documented, version-controlled library of reusable pipeline patterns for MiFID II data flows
  • Faster audit onboarding due to self-documenting data lineage and metadata tagging
  • Cross-functional teams adopting your patterns as the default for compliance-adjacent work
  • Reduced rework when control expectations shift or new instruments are added to scope
  • Proven approach to turn one-off data fixes into long-term, repeatable control assets

The 12 modules (with all 144 chapters)

Module 1. Foundations of MiFID II Data Reporting
Establish a shared understanding of MiFID II’s data transparency requirements, including transaction reporting, best execution, and instrument reference data. Focus on how DataOps bridges legal mandates and technical implementation.
12 chapters in this module
  1. Understanding MiFID II’s RTS 27 and RTS 28 reporting obligations
  2. Defining the scope of reportable transactions and venues
  3. Mapping MiFID II requirements to data pipeline inputs
  4. Key differences between MiFID I and MiFID II data granularity
  5. Regulatory timelines and reporting frequency by asset class
  6. Common misconceptions about pre- and post-trade transparency
  7. Interplay between MiFID II and national regulator guidance
  8. How ESMA guidelines shape local implementation
  9. Data ownership boundaries between front office and DataOps
  10. Integrating legal definitions into technical data dictionaries
  11. Version control for evolving regulatory interpretations
  12. Setting baseline compliance metrics for pipeline performance
Module 2. Designing Audit-Ready Data Pipelines
Learn to build pipelines with embedded audit logic, ensuring every transformation step is justified, documented, and defensible under regulator review.
12 chapters in this module
  1. Embedding lineage capture at ingestion points
  2. Automated logging of data schema changes over time
  3. Designing for reproducibility under regulatory scrutiny
  4. Capturing metadata context with every data hop
  5. Validating data completeness before downstream use
  6. Handling NULLs and defaults without breaking traceability
  7. Documenting assumptions in transformation logic
  8. Tagging regulatory purpose for each pipeline output
  9. Creating audit handover packages automatically
  10. Designing for immutability in reportable data sets
  11. Schema versioning aligned with compliance cycles
  12. Building early-warning flags for lineage gaps
Module 3. Data Lineage and Provenance Tracking
Implement end-to-end lineage frameworks that map raw inputs to final reports, enabling fast response to regulator inquiries and reducing audit prep time.
12 chapters in this module
  1. Defining critical data elements under MiFID II
  2. Mapping data origin to final reporting outputs
  3. Automating lineage capture in Apache Airflow DAGs
  4. Using OpenLineage for standardised tracking
  5. Storing lineage metadata in central registries
  6. Querying data paths during regulator inquiries
  7. Visualising lineage for non-technical stakeholders
  8. Validating lineage completeness as a pipeline gate
  9. Handling data merges and splits in provenance
  10. Integrating lineage checks into CI/CD pipelines
  11. Benchmarking lineage coverage across teams
  12. Reducing audit response time with pre-built queries
Module 4. Schema Enforcement and Data Quality Gates
Enforce schema consistency and data integrity at every stage to prevent compliance drift and ensure reliable downstream reporting.
12 chapters in this module
  1. Defining canonical schemas for MiFID II fields
  2. Validating trade timestamp precision at ingestion
  3. Enforcing ISO 20022 standards for counterparty IDs
  4. Automated rejection of malformed reportable events
  5. Building version-aware schema migration paths
  6. Handling optional vs. mandatory fields consistently
  7. Data quality dashboards for ongoing monitoring
  8. Alerting on schema deviation in production pipelines
  9. Standardising date and time zone handling
  10. Validating best execution data across trading venues
  11. Testing schema resilience under edge cases
  12. Documenting exceptions with audit trails
Module 5. Automating RTS 27 and RTS 28 Reporting Flows
Streamline the generation of periodic transparency reports with pipelines that auto-collect, aggregate, and format required data.
12 chapters in this module
  1. Extracting best execution data from trading systems
  2. Aggregating execution quality metrics by instrument
  3. Normalising data across execution venues
  4. Calculating slippage and market impact indicators
  5. Formatting outputs to meet RTS 27 templates
  6. Automating RTS 28 client brokerage disclosures
  7. Validating report completeness before submission
  8. Scheduling batch runs aligned with reporting cycles
  9. Handling currency conversion in cross-border reports
  10. Redacting sensitive client data in disclosures
  11. Testing report logic against historical data
  12. Generating internal review packages alongside reports
Module 6. Cross-System Data Consistency
Ensure data harmony between front office, risk, and compliance systems to eliminate reconciliation gaps and support unified reporting.
12 chapters in this module
  1. Mapping trade lifecycle events across systems
  2. Synchronising instrument master data globally
  3. Validating trade capture against order management
  4. Reconciling position data with clearing records
  5. Handling trade amendments and corrections
  6. Aligning event timestamps across time zones
  7. Detecting and resolving data drift automatically
  8. Building golden record pipelines for reference data
  9. Integrating market data feeds into compliance pipelines
  10. Standardising data formats for internal sharing
  11. Monitoring cross-system consistency daily
  12. Creating audit trails for reconciliation actions
Module 7. Metadata Management for Compliance
Implement centralised metadata practices that make regulatory data easy to find, understand, and justify.
12 chapters in this module
  1. Defining metadata standards for MiFID II fields
  2. Storing definitions in searchable registries
  3. Linking pipeline logic to regulatory clauses
  4. Automating metadata extraction from code
  5. Building self-documenting data dictionaries
  6. Tagging data with regulatory purpose codes
  7. Integrating metadata into lineage visualisations
  8. Enforcing metadata completeness in PR checks
  9. Versioning metadata alongside code changes
  10. Training compliance teams to use metadata tools
  11. Auditing metadata accuracy quarterly
  12. Reducing onboarding time with clear documentation
Module 8. CI/CD for Compliance-Critical Pipelines
Adapt DevOps practices to ensure regulatory pipelines are tested, reviewed, and deployed with confidence.
12 chapters in this module
  1. Treating schema changes like code changes
  2. Building automated compliance checks in CI
  3. Testing pipeline outputs against spec samples
  4. Requiring peer review for production deployments
  5. Using feature flags for gradual rollouts
  6. Validating data quality in staging environments
  7. Blocking deploys with broken lineage
  8. Integrating static analysis for SQL pipelines
  9. Maintaining audit logs of deployment events
  10. Rolling back safely when issues arise
  11. Documenting changes for regulator access
  12. Building deployment playbooks for on-call
Module 9. Responding to Regulator Inquiries
Prepare structured, fast responses to data-related inquiries using pre-built lineage, validation, and documentation assets.
12 chapters in this module
  1. Receiving and triaging regulator data requests
  2. Locating relevant pipelines and data sources
  3. Extracting lineage paths for specific records
  4. Validating data accuracy with source checks
  5. Compiling evidence packages efficiently
  6. Redacting sensitive information appropriately
  7. Meeting tight response deadlines
  8. Coordinating across legal, compliance, and tech
  9. Documenting responses for future reference
  10. Improving processes based on feedback
  11. Benchmarking response time across teams
  12. Building templates for common inquiry types
Module 10. Scaling Compliance Patterns Across Teams
Turn successful pipeline designs into reusable templates that elevate standards across the organisation.
12 chapters in this module
  1. Identifying high-impact pipeline patterns
  2. Generalising solutions for broader use
  3. Publishing internal documentation
  4. Creating onboarding kits for new teams
  5. Gathering feedback from early adopters
  6. Improving templates based on usage data
  7. Measuring adoption across business units
  8. Recognising teams that contribute patterns
  9. Integrating templates into CI/CD tooling
  10. Maintaining central support for shared assets
  11. Updating templates with regulatory changes
  12. Reducing duplication through reuse
Module 11. Sustaining Compliance Through Change
Ensure data pipelines adapt to regulatory updates, organisational shifts, and technical evolution without losing control.
12 chapters in this module
  1. Tracking regulatory change proposals
  2. Assessing impact on existing pipelines
  3. Prioritising updates based on risk
  4. Planning phased implementation
  5. Testing changes in isolated environments
  6. Communicating changes to stakeholders
  7. Deprecating old pipelines safely
  8. Updating documentation and training
  9. Monitoring performance after changes
  10. Capturing lessons in internal wikis
  11. Building resilience into core designs
  12. Creating runbooks for recurring updates
Module 12. Building a Compounding DataOps Practice
Institutionalise practices that ensure every project strengthens the next, creating lasting technical and organisational leverage.
12 chapters in this module
  1. Reviewing pipelines for reusable components
  2. Cataloging assets in internal registries
  3. Measuring rework reduction over time
  4. Celebrating compounding efficiency gains
  5. Mentoring others in compliance patterns
  6. Presenting success stories to leadership
  7. Integrating feedback into design systems
  8. Funding improvement through savings
  9. Documenting institutional knowledge
  10. Ensuring continuity across team changes
  11. Aligning with strategic data governance
  12. Positioning DataOps as a compliance enabler

How this maps to your situation

  • MiFID II pipeline design and audit readiness
  • Data lineage and cross-system consistency
  • Regulatory inquiry response and collaboration
  • Scaling and sustaining compliance practices

Before vs. after

Before
Rebuilding similar compliance pipelines from scratch each cycle, with limited documentation and inconsistent standards
After
A growing library of reusable, auditable data assets that reduce rework and establish your patterns as the default across teams

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 2.5 hours per module, designed for integration into active project work , apply each lesson directly to current pipelines.

If nothing changes
Without standardised, reusable data patterns, teams continue to rebuild compliance pipelines from scratch , wasting effort, increasing audit risk, and missing the chance to turn individual wins into lasting organisational leverage.

How this compares to the alternatives

Unlike generic data engineering courses, this program focuses exclusively on MiFID II compliance patterns, delivering targeted, field-tested frameworks used in global financial institutions. No theoretical modules , every chapter includes deployable code structures and regulatory mappings.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior MiFID II experience required?
No. The course starts with foundational concepts and builds to advanced implementation, suitable for engineers new to MiFID II or refining their approach.
Can I apply this to non-MiFID II reporting?
Yes. The patterns are designed for reuse in other regulatory contexts like EMIR, SFTR, or Dodd-Frank with minimal adaptation.
$199 one-time. Approximately 2.5 hours per module, designed for integration into active project work , apply each lesson directly to current pipelines..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee· 144 chapters· Hand-built playbook included· Account access within 24 hours
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