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Bank Data Analytics Jobs in Baltimore, MD (NOW HIRING)

... the bank's data storage systems. * Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and design scalable data solutions that support advanced ...

Support banking access management and issue resolution * Resolve operational issues (e.g., rejected ... Bachelor's degree (major in Business, Finance, Computer Science, Data Analytics/Statistics, or ...

SQL Developer (Entry-Level)

Baltimore, MD ยท On-site

$49 - $67/hr

Data scientists and analysts depend on clean, accessible, reliable data--and data engineers help ... Paypal, Banking, Wayfair, Client, Client and hundreds more with Job offers of $95k to $154k.

Data Modeler

Baltimore, MD ยท On-site +1

$54.50 - $70.50/hr

MUST HAVE HAD EXPERIENCE WITH MULTIPLE FINANCIAL/BANKING CLIENTS . * Experience developing conceptual, logical, and physical data models for databases, data warehouses, and analytics systems.

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Bank Data Analytics information

How does a bank data analytics professional typically collaborate with other departments within a financial institution?

Bank Data Analytics professionals work closely with various departments such as risk management, marketing, compliance, and IT. They translate complex data sets into actionable insights, guiding strategic decisions and helping teams understand customer behavior, detect fraud, and ensure regulatory compliance. Regular cross-functional meetings and project-based collaborations are common, allowing analytics professionals to align data-driven recommendations with business goals and operational needs. This collaborative structure enhances communication, streamlines workflow, and maximizes the value of data across the organization.

What is bank data analytics?

Bank data analytics is the process of collecting, processing, and analyzing large volumes of data generated by banking transactions and operations. It helps banks gain insights into customer behavior, detect fraud, manage risks, and improve decision-making. By leveraging advanced analytical tools and techniques, banks can enhance customer experiences, increase efficiency, and develop data-driven strategies for growth. Bank data analytics professionals work with big data, machine learning, and statistical models to extract meaningful patterns and support business objectives.

What is the difference between Bank Data Analytics vs Bank Data Analyst?

AspectBank Data AnalyticsBank Data Analyst
Required SkillsData analysis, statistical modeling, programming (SQL, Python)Data analysis, reporting, basic statistical skills
Work EnvironmentData teams, analytics departments within banksBank branches, finance departments, risk management teams
CertificationsData analytics certifications, SQL, Python coursesFinance or banking certifications, possibly data skills
Industry UsageFocus on developing analytics models and insightsFocus on interpreting data for decision-making

Bank Data Analytics involves advanced data modeling and technical skills to develop insights, while a Bank Data Analyst primarily interprets data to support banking operations. Both roles require analytical skills, but Bank Data Analytics is more technical and model-driven, whereas Bank Data Analyst focuses on reporting and data interpretation within banking environments.

What are the key skills and qualifications needed to thrive as a bank data analytics professional, and why are they important?

To thrive as a Bank Data Analytics professional, you need strong analytical skills, proficiency in statistics, and a solid background in finance or economics, often supported by a relevant degree. Expertise in data analysis tools such as SQL, Python, R, and experience with business intelligence platforms like Tableau or Power BI, as well as knowledge of data governance frameworks, is highly valued. Strong problem-solving abilities, attention to detail, and effective communication help translate complex data insights into actionable recommendations for stakeholders. These skills are crucial for driving data-informed decisions that enhance financial performance and risk management in the banking sector.
What job categories do people searching Bank Data Analytics jobs in Baltimore, MD look for? The top searched job categories for Bank Data Analytics jobs in Baltimore, MD are:
Infographic showing various Bank Data Analytics job openings in Baltimore, MD as of August 2026, with employment types broken down into 83% Full Time, 9% Part Time, 4% Temporary, and 4% Contract. Highlights an 87% In-person, 4% Hybrid, and 9% Remote job distribution.

Data Engineering Team Leader

Orrstown Bank

Towson, MD โ€ข On-site

Full-time

Posted 4 days ago


Job description

Position Summary:

The Data Engineering Team Leader is responsible for innovating, operating, influencing, and delivering systems architectures in support of our business. This position helps shape the technology strategy and alignment with corporate strategic goals. Responsibilities include (1) managing all aspects of system design, deployment, maintenance, and support for all systems within area of responsibility, (2) recruit, retain, and develop an exceptional systems engineering team, (3) create and refine operational processes to ensure effective security and compliance, (4) strengthen user experiences, service levels, and business relationships. The Data Engineering Team Leader will provide technical leadership, collaborate with cross-functional teams, and drive innovation in data engineering practices to support the bank's strategic objectives.

Qualifications:


  • Bachelor's degree in Computer Science, Information Systems, or a related field. A master's degree is preferred.
  • Minimum of seven years of systems engineering experience, with a focus on designing and implementing complex data solutions, preferably in the banking or financial industry.
  • Strong proficiency in SQL and experience with various relational databases (e.g., Oracle, SQL Server, MySQL) and related technologies.
  • Expertise in programming languages such as Python, Java, or Scala.
  • Solid understanding of data engineering concepts, techniques, and best practices.
  • Experience with big data technologies such as Hadoop, Spark, or similar frameworks.
  • In-depth knowledge of data integration, ETL processes, and data transformation techniques.
  • Proficiency in data modeling and database design principles.
  • Strong familiarity with cloud platforms like AWS, Azure, or Google Cloud, including relevant data services (e.g., Snowflake, Redshift, BigQuery).
  • Deep understanding of data governance, data quality, and data security practices, including regulatory compliance requirements.
  • Experience with data pipeline orchestration tools (e.g., Apache Airflow, Azure Data Factory, Qlik Talend)
  • Strong problem-solving and analytical skills.
  • Excellent communication and collaboration skills.
  • Ability to lead and mentor a team of data engineers.
  • Strong organizational and project management abilities.
  • Ability to adapt to changing technologies and work effectively in a dynamic environment.
  • Maintains high standards of personal integrity and accountability

Core Competencies:

Career Development: Proactively prepares and actively participates in ongoing, candid, constructive monthly Coaching sessions with supervisor. Seeks advancement into challenging and developmental roles and assignments. Sets and meets clear, measurable goals.


Communicates Effectively: Demonstrates the ability to effectively communicate with all employees and clients, regardless of level. Communicates clearly, concisely, with candor and confidence. Writes, speaks and listens to disseminate and receive information effectively and accurately. Seeks to understand the viewpoints of others. Keeps others informed in a timely manner.


Focuses on the Client: Anticipates and identifies internal and external client needs. Takes action to meet and, where possible, exceed client expectations. Plans and organizes work effectively to facilitate responsiveness to client and meet deadlines.


Judgment: Demonstrates sound reasoning and well-balanced thinking. Balances the need for action with the need for analysis. Incorporates strategic thinking skills into practice by examining facts. Shows an ability to problem solve complex issues. Probes beyond symptoms to determine the underlying cause. Learns from and accepts responsibility from mistakes.


Teamwork: Demonstrates the ability to enhance the department and Orrstown Banks development through participation. Holds self and others accountable for exceeding departmental and corporate goals. Develops strong working relationships throughout the organization. Appropriately voices opinions, even if they are contrary to the consensus of the team. Initiates and develops positive working relationships with others in a way that builds bridges across boundaries and breaks down silos. Relates to others in an open and accepting manner that creates trust, respect and a collaborative environment.



Essential Duties:

  • Lead the design, development, and maintenance of the bank's data infrastructure, including data pipelines, databases, and data warehouses.
  • Architect and optimize ETL processes to ensure efficient extraction, transformation, and loading of data from diverse sources into the bank's data storage systems.
  • Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and design scalable data solutions that support advanced analytics and reporting needs.
  • Implement and enforce data governance policies, ensuring data quality, consistency, security, and compliance with regulatory requirements.
  • Perform advanced data modeling and database design activities, optimizing data structures for integration, storage, and retrieval.
  • Lead performance tuning and optimization efforts to enhance data engineering systems' scalability, reliability, and throughput.
  • Stay abreast of emerging technologies, industry trends, and best practices in data engineering, and make recommendations for their application within the bank's environment.
  • Provide technical leadership, guidance, and mentorship to junior data engineers, fostering a culture of continuous learning and professional development.
  • Collaborate with IT teams to ensure seamless integration of data engineering solutions with existing infrastructure and applications.
  • Document data engineering processes, workflows, configurations, and system architectures.

Physical Requirements:

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

While performing the duties of this job, the employee is regularly required to sit, use hands to finger, handle or feel, reach with hands and arms, and talk or hear. The employee is frequently required to stand and walk. The employee may occasionally lift and/or move up to 10 pounds. Ability to reach destinations within the Orrstown footprint at all times is required. Ability to work and report to the employer’s physical work location(s).   

Work Environment:

Work is performed in an office setting with little to moderate exposure to noise, heat, dust or other adverse factors. Working extended hours may be required as needed. The noise level in the work environment is usually quiet.