Data Ops - Data Builder
About the role
Data Management Strategy & Governance Advisor
, offers an exciting opportunity to build on your strengths, expand your impact, and continue growing within the organization.
Job Description
- The Data Builder is the hands-on builder responsible for turning architectural design and business requirements into
trusted, reusable data products through MVP that are production grade.
co-designs and builds Gold data products
where business logic, metrics, and functional knowledge live.
- This is a hands-on individual contributor role - the
build authority
business fluency and data engineering discipline in equal measure
, authoring Gold data products that answer the question the first time and building them to production standard.
- This role is a critical execution counterpart to
Data Architecture and Data Analysis, Products & Integration
, building faithfully against their designs and requirements and compressing the path from new data to a production-grade, reusable asset. It is an exciting opportunity to join the team transforming how Cigna builds and reuses data - delivered through MVP. Key Responsibilities Rapid Build & Delivery
- Build architecture's designs and analysis's requirements into
production-ready solutions
through MVP.
- Work new and unfamiliar data quickly into a
working solution
that answers the business question at hand.
- Refactor
learning-stage and bespoke work
into durable, reusable, production-ready assets. Silver - Build to Architecture's Design
- Build the physical Silver structures to the
enterprise-agnostic Silver model
and reference patterns designed by Data Architecture.
- Implement the design faithfully and promote it to production through
governed CI/CD pipelines
.
- Capture
business context and metadata
at build, and hand off to trusted partner teams as readiness allows. Gold - Democratized, Delivered Through MVP
- Treat the Gold layer as
democratized
- Data Operations may own specific Gold assets and assists other teams in building theirs.
- Deliver Gold solutions through
build-through-MVP
, promoted to production-grade through governed CI/CD pipelines regardless of owner.
- Uphold the
build standards and guardrails
any Gold solution must meet, so a federated Gold layer stays stable and production-grade across owning teams. Engineering Discipline & Production Readiness
- Build to
production standards
from day one so the lift from MVP to production is simple, not a rebuild.
- Apply consistent
development patterns, naming conventions, and build standards
with specific focus on the connectivity across all data products to create a more connected data ecosystem.
- Move work through the CI/CD pipeline - develop, test, and productionize - clearing
quality, security, and cost checks
on first pass.
- Apply
AI and automation
to accelerate build and testing without bypassing governance, security, or quality expectations. Partnership & Collaboration
- Partner with
Data Architecture
- building to their Silver design and co-designing Gold within the standards they set.
- Partner with
Data Analysis, Products & Integration
- who bring the requirements, business rules, and acceptance criteria this role builds against.
- Partner with
Platform Engineering / IT and DevOps & AI Enablement
- who own ingestion, pipelines, and the automation frameworks this role builds on.
- Partner with
data science and business analytics teams
so Gold data products serve their models, analyses, and decisions.
- Set the design and hold Data Operations standards across
the Hyderabad Data Operations team
outside service partners
as part of a global delivery model. What This Role Is Not
- Not a pure data engineering or platform role.
- Not the owner of the enterprise Silver model.
- Not the primary business-engagement or requirements role.
- Not the owner of the Gold layer, which is democratized across teams.
- Not a governance policy, stewardship, or BI reporting role.
- This role exists to
build real data solutions that get used
3+ years building production-grade data solutions
, with demonstrated depth in analytics engineering or equivalent business-plus-build work.
- A genuine
50/50 blend of business fluency and hands-on engineering discipline
.
- Hands-on experience with
Databricks, Spark, Unity Catalog, SQL, and Python
.
- Familiarity with
Snowflake and Teradata
in addition to a Databricks-first stack.
- Expert knowledge of
dimensional and medallion (Silver / Gold) modeling
within an enterprise-agnostic data model.
- Working knowledge of
CI/CD pipelines, automated code evaluation, and controlled promotion
to production.
- Strong
software-engineering discipline
Performance-tuning and cost-optimizing Spark and SQL transformations
at scale.
- Familiarity with
data governance and cataloging tooling
Automated data-quality testing and validation
built into every solution.
- Demonstrated ability to
learn a new data domain fast
and turn business logic into reusable data products.
- Demonstrated ability to apply
AI and automation
to accelerate build while preserving quality and governance.
- Experience in
complex, regulated enterprises
seeyourself@thecignagroup.com
Not included in the source posting: qualifications, benefits.
Skills
Who can apply
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