> Data Analytics Engineer

Location: Remote

Work Mode: Remote

Skills: SQL;CI / CD;Snowflake;DBT;Fivetran;Data Analytics

Experience in Years: 6

Type: Full Time

Life Sciences Cloud, Revenue Lifecycle Management and nCino each carry configuration, data dependencies and business journeys that must move-and be verified-with every release.

A Salesforce release is rarely just a collection of metadata. In an industry cloud, the change also carries business configuration, connected data, sequencing rules and critical journeys that have to remain intact from one environment to the next.

That distinction matters. A deployment can complete exactly as planned while the wider business release still contains unresolved risk: a configuration dependency moved out of sequence, target data is not ready, or a process that spans several objects no longer behaves as expected.

An industry-aware DevOps operating model makes those concerns part of the release itself. It combines the delivery automation already in Copado with reusable assets configured around the product, the customer s environments and the business process being changed.

The release is not complete when the metadata deploys. It is complete when the industry process still works. The release boundary has changed

Traditional release planning often begins with a list of components and ends with a successful promotion. Industry-cloud delivery asks a broader set of questions:

- Which configuration and connected data must travel with the code
- In what order must dependent assets move
- What must be true in the target environment before promotion
- Which critical business journeys need regression coverage
- Which controls should change based on release type, risk or policy

These are not separate operational checks to be added at the end. They are part of the delivery design. The practical answer is an accelerator that packages three connected layers: data and configuration movement, release quality gates and Copado Robotic Testing (CRT) regression coverage.

The Industry DevOps operating model

Different industry processes. One connected release model.

Life Sciences Cloud Programs and regulated delivery

Care Programs, HCP Engagement, consent, participant management and patient-service journeys.

Revenue Lifecycle Management Product-to-order delivery

Product, pricing, order and runtime configuration across the revenue lifecycle.

nCino Controlled banking change

Banking configuration, connected data movement and regression for critical workflows.

1 Data and configuration packs Preserve the relationships and sequence required by the industry process.

2 Release quality gates Evaluate dependencies, configuration integrity, target readiness and promotion policy.

3 CRT regression coverage Verify the critical business journeys affected by the change.

Copado delivery foundation Data Deploy Quality Gates Robotic Testing

The accelerator layers are configured to the customer s object model, environments, release path and governance rules. What industry-aware means in practice

The model is consistent, but the implementation should not be generic. Each industry product brings its own release surface and its own definition of continuity.

Life Sciences Cloud: protect connected programs and journeys

A Life Sciences Cloud change can touch Care Programs, HCP Engagement, consent, participant management, patient services, and product and order data. The delivery challenge is not simply moving each item. It is preserving the relationships that allow a regulated process to work as intended.

An accelerator for Life Sciences Cloud can package the relevant configuration and data, check for drift or conflict, confirm target deployability and run CRT coverage across Care Program enrollment, HCP engagement, consent and patient-service journeys.



The release team sees the controls in the same flow as the promotion-not in a disconnected checklist.

Revenue Lifecycle Management: keep the lifecycle synchronized

Revenue Lifecycle Management connects product, pricing, order and runtime configuration. These elements form a dependency chain; treating them as unrelated deployment items creates avoidable uncertainty around sequencing and target readiness.

An industry-aware model packages the connected configuration, applies dependency and promotion gates, and tests the product-to-order journey after the change. That gives release leaders a clearer answer to the real question: did the revenue process remain coherent across configuration and runtime behavior

nCino: govern configuration and connected data together

For nCino, banking configuration and connected data must move under controlled promotion. A technically successful component deployment is only one part of that change.

The accelerator packages the relevant configuration and data, validates them before promotion, and applies CRT coverage to the banking workflows that matter after the release. The result is a repeatable control model that supports delivery speed without losing visibility into operational risk.

Three layers make the accelerator operational 1. Data and configuration packs

The first layer defines the assets that travel together. It captures the object relationships, configuration scope and required movement sequence for the industry process. Instead of rebuilding that knowledge for every release, the team has a reusable package that can be maintained as the implementation evolves.

2. Release quality gates

The second layer turns release knowledge into executable control. Gates can assess dependencies, data movement, configuration integrity, target readiness and promotion policy at the relevant point in the path to production. This makes the control visible, repeatable and tied to the release record.

3. CRT regression coverage

The third layer validates the outcome at the level the business recognizes: the journey. The regression scope follows the affected process-Care Program enrollment, product-to-order, or a critical banking workflow-so the evidence says more than the deployment passed.

The operating principle

Move the connected assets, enforce the right control and verify the affected journey. The three layers work as one release model, not as independent tools or after-the-fact checks.

An accelerator should fit the customer-not force a template

Reusable does not mean fixed. A useful industry accelerator is configured to the customer s object model, environments, release path and governance rules. It should make a clear distinction between what can be configured directly and what requires extension.

That distinction is essential because two customers on the same industry product may have different operating models, risk thresholds and release calendars. The value of the accelerator is the combination of proven delivery assets and a disciplined configuration process-not a claim that every implementation is identical.

Once configured, the model also needs an owner. Through Copado as a Service, CloudFulcrum can operate deployment execution, sandbox operations, asset and gate maintenance, CRT suite extension, release reporting and readiness for platform releases. The accelerator becomes part of the day-to-day release system rather than a one-time implementation artifact.





Start with one representative release problem

The fastest way to determine whether an industry accelerator is useful is to apply it to a real change. A focused working session should leave the team with a delivery blueprint it can evaluate, including:

- Industry object and configuration scope
- Quality gates mapped to policy and risk
- CRT scope for affected business journeys
- Deployment sequence and dependencies
- Direct configuration versus required extension
- An operating path after initial configuration

This moves the conversation from broad capability to release engineering. The customer can see where the accelerator fits, which assets are reusable and what must be adapted to the implementation.

Industry clouds deserve a release model built around the process they run. When data and configuration movement, quality gates and regression coverage are designed together, Copado becomes the foundation for a more complete and repeatable industry delivery system.

Bring one industry release problem.

Leave with the delivery blueprint-covering the connected assets, release gates, CRT scope and deployment sequence for a representative change.

Book Your Accelerator Session About CloudFulcrum CloudFulcrum helps enterprises engineer and operate Salesforce delivery models that connect release automation, quality controls and business-process assurance. Learn more about CloudFulcrum s Salesforce DevOps capabilities .

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Data Analytics Engineer

-Experience : 6+ years

-Work Mode : Remote, India

We need an experienced Data Analytics Engineer to build and scale our data pipelines and data architecture. Youll take business questions, figure out what data is needed to answer them, and build the pipelines and models to make that possible in an optimized and efficient way. This role expects strong fundamentals in SQL, Snowflake, and dbt, along with fluency in using AI tools and coding assistants to work faster and more effectively.

Key Responsibilities

- Build and maintain data pipelines and models that support analytics, reporting, and BI.
- Monitor and improve existing pipelines in Snowflake and dbt - fix whats slow, expensive, or unreliable.
- Dig into data anomalies and pipeline issues to find the actual root cause, not just patch the symptom.
- Use AI tools and coding assistants day-to-day to write boilerplate, speed up queries, and move faster through development.
- Explain your work clearly to both engineers and non-technical stakeholders - product, finance, whoever needs the data.
- Write tests and documentation so the warehouse stays reliable and other people can trust and build on your work.

Required Qualifications

- 6+ years of experience in data engineering or analytics engineering.
- Advanced hands-on experience with SQL, Snowflake, dbt, and Fivetran.
- Track record of picking up new tools and adapting quickly in a fast-moving environment.
- Able to dig past the surface of a problem to understand whats actually driving it, not just execute whats asked.
- Regular use of AI tools as part of your workflow.
- Strong written and verbal communication , can explain technical decisions in plain business terms.
- Bachelors degree in Computer Science, Data Science, or related field.

Nice to Have

- Master s Degree in Computer Science, Data Science, or a related technical field
- Experience with version control workflows (git), CI/CD for data pipelines and Business intelligence tools like Tableau, Power BI, Sigma or Hex.
- Prior experience working in a rapid-moving analytics org.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Analytics Engineer (India)
🏢 CloudFulcrum
📍 India