Senior Data Analyst

About the Company:

Stable Kernel is a technology services firm and custom software developer building scalable software solutions for cutting-edge, innovative enterprises to move their business forward. We are headquartered in Atlanta, GA.

We’re a privately held, Great Place to Work Certified Company™ with multi-awards for our amazing culture. Plus we have an impressive 10+ year trajectory of sustainable growth. At Stable Kernel, we support our colleagues in ways that help them do some of the best work of their lives.

About the Role:

As a Stable Kernel Senior Data Analyst, your primary goal is to aggregate and analyze client data to derive insights and recommendations for business optimization; communicating out recommendations to clients and the supporting team. Your special blend of technical knowledge, problem-solving, analytical, and communication skills allow you to work with our clients’ product owners to answer if their most innovative projects operate as intended and effectively solve business problems. On data migration engagements, you will work alongside data engineers to validate, specify and prove out the move of reporting and data assets onto modern cloud platforms

Senior Data Analysts report directly to our Director of Data Strategy.

Core Responsibilities Include:

Technical Impact:

Designing and implementing solutions with an eye to the growth of the product and team by improving the speed of future work and preserving optionality without unnecessary complications.

Owning end-to-end design of measurement frameworks for complex data structures and driving standardization across projects.

Leading creation of advanced analytics strategies based on data-driven insights.

Utilizing advanced-level capabilities and supporting team members by crafting reusable solutions within the existing toolset.

Evaluating and recommending tooling improvements and analytics platforms for adoption across teams.

Working across teams to build reusable templates and advanced workflows that are easy to implement across clients and projects.

Understanding of cross-departments practices, products and infrastructure (e.g. APIs, cloud infrastructure, basic web architectures, native vs. cross-platform mobile solutions, data structures, and databases).

Anticipating cross-functional impacts and advocating for data considerations in product and strategy discussions.

Bridging communication gaps between business, engineering, and executive stakeholders.

Business Alignment :

Understanding the client’s business and goals to improve the definition for your team’s projects.

Refining and challenging business questions to ensure data solutions and insights drive value.

Aligning team efforts with long-term client goals.

Facilitating team conversations to clearly identify gaps, risks, and assumptions to accurately estimate work.

Extending the expectations of your role to business processes in order to identify underlying needs and opportunities.

Understanding of the Product Owner role and responsibilities and having the ability to operate as one.

Understanding of SK’s service offerings and cross-department capabilities.

Identifying new organic growth opportunities with the client and engaging them in next steps.

Supporting the statement of work process with the ability to craft sections of the SOW independently for growth in the data capability.

Interacting with Others:

Coordinating, leading and collaborating on analytics initiatives with others outside of your team including product and design leadership.

Influencing strategic decisions at the project and organization level. Guiding roadmap discussions with data-backed perspectives.

Advising people across your VP-level organization. Acting as a key advisor to stakeholders and executives.

Delivering strategic-level status and team reports to leadership and senior clients.

Consulting:

Leveraging expertise to drive strategic growth by championing new analytics solutions or service offerings.

Acting as the subject matter expert in high-level client discussions.

Working with the client to prioritize data-led initiatives within the product backlog and representing their needs in planning sessions.

Succeeding with ambiguity by your ability to plan, structure, and execute complex solutions without much input.

Shaping problem definitions, measurement frameworks, and establishing business goals to align teams early.

Leading Discovery sessions that inform product roadmap and decisions that are founded in data.

Problem-Solving:

Demonstrating a clear understanding of inductive and deductive reasoning skills when driving solutions for products.

Identifying and solving high-priority, complex problems and distilling them into short and long term solutions quickly and effectively.

Understanding of end-user and client goals to inform product direction.

Leading problem solving workshops to align client teams.

Recognizing and mitigating risk effectively as well as removing roadblocks.

Anticipating future risks at initiative or organization level and developing contingency plans to remove risks before problems arise.

Navigating difficult situations by independently finding a way forward or escalating when necessary with a possible solution.

Leadership:

Taking ownership of internal projects to streamline team processes that assist with speeding up and/or simplifying the work.

Driving best practices across your project and client program.

Mentoring junior and intermediate colleagues and supporting their professional growth.

Setting a positive example.

Providing regular constructive feedback.

Guiding less experienced colleagues through complex projects.

Leading team learning sessions.

Resolving conflicts.

Demonstrating strong product leadership skills, including planning, delegating, and monitoring progress.

Making critical decisions and driving deliverables to successful implementation.

Representing the team in client conversations.

Data Migration Delivery

Validating the existing reporting and data consumption estate before it is migrated.

Reviewing each asset against the client’s metadata repositories and usage data to confirm ownership, consumers and source systems.

Reverse-engineering business logic embedded in BI tools and documenting it as business rules from client owners to confirm.

Proving parity between legacy and target environments.

Defining and running reconciliation checks and assembling the evidence for client sign off.

Supporting extended parallel runs for finance and SOX related reporting with Finance and audit stakeholders.

Partnering with client Data Product Owners and business users through cutover.

Defining gold-layer requirements, data contracts, and data quality rules with Data Product Owners.

Running consumer interviews, user acceptance testing, and support training for migrated reports and dashboards.

Intimate, Working Familiarity With:

Familiarity with data warehouses, product analysis, statistical analysis tools and experience with large data sets and has managed their reliability and consistency.

Experience with Customer Engagement Platform RM tools such as MoEngage or Braze.

Experience with reverse ETL and tools such as Hightouch.

Experience using SQL and Python.

Experience with Databricks and medallion architecture data models, or comparable lakehouse platforms.

Experience with observability and BI tools such as DataDog, Anomalo, Tableau, Cognos, Looker or PowerBI.

Experience using implementation and analytics related tools such as Google Tag Manager, Google Analytics, Pendo or Amplitude.

Familiarity with Microsoft Office Suite.

General Requirements:

5-8 years of experience as a data analyst.

Experience working in a consulting or professional services organization.

Experience supporting a data migra