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Director Data Analytics Biotech Jobs in Texas (NOW HIRING)

Senior Director, Data Quality Requisition ID: 270860 Salary Range: - Please note that the Salary ... Lead root-cause analysis and continuous improvement efforts for recurring data quality issues ...

... analytics to data-driven operational automation - while building the engineering culture ... Please direct any other general recruiting inquiries to our Contact Us page > I want to work for ...

- We are looking for a Director, Data Engineering, to lead the continued optimization and maturation ... analytics teams can reliably and efficiently access high-quality data. Reporting to the VP-Data ...

## Director Data Product ManagementApplylocations: TX, Coppelltime type: Full timeposted on: Posted ... Lead the design of unified data capabilities that power analytics, ML/AI, real-time systems, and ...

Establishes standards for trusted data products and ensures data consumed by analytics, AI, and agents is authoritative, traceable, and fit for purpose. * Defines governance across the AI/ML ...

Showing results 41-60

Director Data Analytics Biotech information

What does a director of data analytics do in the biotech industry?

A Director of Data Analytics in biotech leads teams that analyze large and complex biological and clinical datasets to support research, product development, and business decisions. They design data strategies, oversee data management, and implement analytics tools to extract meaningful insights from scientific data. Their work often supports areas like drug discovery, clinical trials, and market analysis, ensuring data-driven decisions throughout the organization. This role requires a strong background in both biotech and advanced analytics, as well as leadership skills to manage cross-functional teams.

What are the typical challenges faced by a director of data analytics in the biotech industry, and how is success measured in this role?

A Director of Data Analytics in biotech often navigates challenges such as integrating complex datasets from various sources, ensuring data integrity, and translating analytics into actionable insights for research and business teams. Success in this role is typically measured by the ability to drive data-informed decision-making, improve efficiency in research pipelines, and support regulatory compliance through robust data practices. Additionally, effective leadership of cross-functional teams and fostering a culture of data literacy across the organization are key indicators of success.

What are the key skills and qualifications needed to thrive as a director of data analytics in biotech, and why are they important?

To thrive as a Director of Data Analytics in Biotech, you need advanced expertise in data science, statistical analysis, and a strong foundation in life sciences, typically supported by a relevant advanced degree (e.g., PhD, MS). Familiarity with tools like Python, R, SQL, cloud computing platforms, and experience with data visualization and bioinformatics systems are essential, along with relevant certifications in data analytics or project management. Strong leadership, strategic thinking, and effective communication skills help you translate complex data into actionable insights and foster cross-functional collaboration. These competencies are critical for driving data-driven decision-making and innovation in a highly regulated, research-focused biotech environment.

What is the difference between Director Data Analytics Biotech vs Data Scientist Biotech?

AspectDirector Data Analytics BiotechData Scientist Biotech
Required CredentialsAdvanced degree (Master's/PhD), leadership experienceTypically Master's or PhD in Data Science, Statistics, or related field
Work EnvironmentStrategic leadership, team management, cross-department collaborationHands-on data analysis, model development, coding, and experimentation
Employer & Industry UsageBiotech companies, research institutions, pharma firmsBiotech firms, research labs, healthcare startups

The main difference is that the Director Data Analytics Biotech focuses on strategic oversight, team leadership, and aligning analytics with business goals, while the Data Scientist Biotech is more involved in technical data analysis, modeling, and coding tasks. Both roles require strong analytical skills and industry knowledge, but the director position emphasizes management and strategic planning.

What are popular job titles related to Director Data Analytics Biotech jobs in Texas?

For Director Data Analytics Biotech jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Director Data Analytics Biotech jobs in Texas look for?

The top searched job categories for Director Data Analytics Biotech jobs in Texas are:

What cities in Texas are hiring for Director Data Analytics Biotech jobs?

Cities in Texas with the most Director Data Analytics Biotech job openings:

Senior Director, Data Quality

Scotiabank

Dallas, TX • On-site

$180 - $240/hr

Other

Posted 14 days ago


Job description

"Title: Senior Director, Data Quality

Requisition ID: 270860


Salary Range: -


Please note that the Salary Range shown is a guideline only. Salary offered may vary based on factors, including, but not limited to, the successful candidate’s relevant knowledge, skills, and experience.


Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.


Global Banking and Markets

Global Banking and Markets (GBM) is a leading Canadian Capital Markets and Investment Banking business with a growing platform in the US and Latin America, operating globally for over 100 years. Scotiabank’s strong U.S. presence provides our clients an important bridge to this key global market for trade and investment flows across the Americas and the world. Global Banking & Markets provides a full range of investment banking, credit and risk management products and services relevant to the financing and strategic development needs of our clients. Our products include debt and equity financing, mergers & acquisitions, corporate banking, institutional equity sales, trading and research, fixed income products, derivatives, energy, foreign exchange and precious & metals. We also cross-sell the full range of wholesale products and services offered by the Scotiabank Group. Be part of an innovative, Global Capital Markets and Investment Banking business with a unique geographic footprint that puts capital to work for our clients across industries! We work together to drive ambition for every future!


Purpose

The Senior Director, Data Quality Engineering, is an enterprise engineering leader accountable for defining, building, and scaling data quality capabilities that strengthen trust in critical data across analytics, AI, regulatory reporting, and operational processes. This role owns the data quality engineering strategy, operating model, roadmap, and execution across a modern Databricks Lakehouse platform, ensuring quality controls are automated, observable, measurable, and embedded directly into the data lifecycle.


What You'll Do

You will partner with senior technology, data, governance, risk, and business leaders to establish enterprise-wide data quality standards, engineering patterns, observability practices, and remediation workflows. You will lead managers and senior engineers responsible for capabilities including profiling, rule management, anomaly detection, quality scorecards, data contracts, incident management, and quality controls integrated into Lakehouse engineering workflows.


Enterprise Data Quality Strategy & Engineering Leadership:

  • Define and own the enterprise data quality engineering strategy, roadmap, and operating model aligned to Lakehouse architecture, including Databricks, Delta Lake, Unity Catalog, metadata services, and the Enterprise Data Catalog.

  • Lead the design, delivery, and continuous improvement of scalable enterprise data quality capabilities, including:

    • Data profiling, quality rule authoring, and rules lifecycle management

    • Completeness, accuracy, validity, uniqueness, timeliness, consistency, and freshness checks

    • Quality thresholds, SLOs, scorecards, and certification criteria for critical data assets

    • Data quality issue detection, triage, ownership, remediation, and evidence capture



  • Set engineering standards that embed automated quality checks into end-to-end data pipelines, including Bronze, Silver, and Gold layers, so issues are detected early, prevented from flowing downstream, and governed through repeatable controls.

  • Influence and align data owners, stewards, engineers, platform teams, security, risk, audit, and senior business stakeholders on quality expectations for critical data elements, data products, and regulatory reporting processes.

  • Drive adoption of reusable data quality frameworks, patterns, templates, APIs, and self-service onboarding models that make quality controls practical for engineering teams to implement at enterprise scale.

  • Ensure data quality controls are measurable, auditable, policy-aligned, and supported by evidence required for regulatory, reporting, operational risk, and executive governance needs.


Data Observability, Monitoring & Operational Trust:

  • Build and operate data observability capabilities that provide visibility into freshness, volume, schema drift, distribution changes, completeness, and reliability across critical pipelines.

  • Implement automated profiling, anomaly detection, alerting, and monitoring to identify quality issues before they impact analytics, AI, reporting, or downstream business processes.

  • Create quality dashboards, scorecards, and service-level indicators that help business and technology stakeholders understand data health, trends, and risk exposure.

  • Lead root-cause analysis and continuous improvement efforts for recurring data quality issues, partnering with source system, pipeline, and product teams to eliminate defects at the source.


Platform Productization, Adoption & Organizational Leadership:

  • Productize data quality capabilities as reusable platform services, including rule libraries, validation templates, metadata-driven controls, and self-service onboarding patterns.

  • Ensure data contracts include explicit quality expectations such as schema, SLA/SLO, freshness, completeness, and acceptance criteria.

  • Promote trusted, certified, and fit-for-purpose data assets by integrating quality signals into catalog, marketplace, and stewardship workflows.

  • Build, lead, and develop a high-performing organization of data quality engineering managers, senior engineers, and specialists; establish talent plans, engineering practices, delivery discipline, and a culture of accountability and continuous improvement.

  • Represent data quality engineering in executive forums by communicating strategy, delivery progress, operational risk, quality trends, investment priorities, and measurable business outcomes.


What You'll Bring

  • Bachelor’s degree in computer science, engineering, information technology, data management, or a related discipline.

  • 12+ years of progressive experience in data engineering, data quality, data management, platform engineering, or related technology disciplines, including 7+ years leading engineering teams and 3+ years managing managers or senior technical leaders.

  • Depth in financial services or other highly regulated industries, with demonstrated experience delivering audit-ready controls, regulatory reporting quality, operational risk reduction, and executive-level governance outcomes.

  • Hands-on experience with:

    • Databricks, Delta Lake, Unity Catalog, workflows, and Lakehouse data engineering patterns

    • Data quality platforms, profiling tools, observability frameworks, rule engines, and monitoring capabilities

    • Metadata-driven controls, catalog integration, lineage-aware quality monitoring, and data contract implementation

    • Strong understanding of data quality dimensions, critical data elements, quality scorecards, SLAs/SLOs, and issue management workflows

    • Practical experience embedding quality controls into batch, streaming, and orchestration workflows

    • Cloud platform experience, with Azure preferred



  • Proven enterprise leadership in data quality engineering, observability, profiling, rule management, monitoring, incident response, remediation, and continuous improvement at scale.

  • Strong understanding of data governance, metadata, lineage, security, privacy, and regulatory compliance as they relate to data quality controls.

  • Demonstrated ability to influence senior executives, architects, data owners, stewards, risk partners, and engineering teams to deliver measurable improvements in data trust, control effectiveness, and business confidence.

  • Exceptional executive communication skills, with the ability to translate complex data quality, platform, and risk topics into clear business impact, investment priorities, operational metrics, and decision-ready recommendations.


Interested?

If your experience is closely related but doesn’t align perfectly with every qualification, we do encourage you to apply - you might be the right candidate for this or other roles at Scotiabank! At Scotiabank, every employee is empowered to reach their fullest potential, respected for who they are and, embraced for their differences. That’s why we work to grow and diversify talent and engage employees in a performance-oriented culture.


What's in it for you?

Scotiabank wants you to be able to bring your best self to work – and life, every day. With a focus on holistic well-being, our many flexible benefit programs are designed to help support your unique family, financial, physical, mental, and social health needs.


#Dallas


Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.


At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know.


We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.


Scotiabank is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by federal, state, or local law.


Nearest Major Market: Dallas


Nearest Secondary Market: Fort Worth


Job Segment: Senior Quality Engineer, Quality Engineer, M&A, Compliance, Quality Manager, Engineering, Management, Legal, Quality

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