Sr Data Engineer

MeridianLink
United States
Remote

Who this role is best for

A natural match if you have experience with financial systems on SaaS platforms and building scalable data pipelines.

Best fit for

  • Candidates with 6-8 years in financial SaaS data engineering and a track record of building scalable ETL pipelines
    — “6-8 years of experience in data engineering, with a strong focus on financial systems on SaaS platforms”
  • Professionals skilled in cloud-based data platforms and distributed processing frameworks like Spark and Databricks
    — “Proven experience with cloud-based data platforms (preferably AWS or Azure)”
  • Individuals who can translate business requirements into data models and workflows for cross-functional teams
    — “Partner with stakeholders across engineering, finance, sales, and compliance to translate business requirements into reliable data models and workflows.”

Things to consider

  • The role requires independent judgment and the ability to handle diverse, complex data challenges
    — “Work is expected to be done independently through independent judgment.”
  • Candidates should be prepared to engage in enterprise-level process improvement and cost reduction initiatives
    — “Working with people, processes facing Enterprise a plus, improvement of processes/reduction of costs in the enterprise processes leveraging data is preferable.”

How to stand out

  • Highlight your experience with real-time and batch processing for financial forecasting and risk analytics
    — “Architect and scale our modern data platform to support real-time and batch processing for financial forecasting, risk analytics, and customer insights”
  • Emphasize your background in data governance, quality, and compliance in financial services contexts
    — “Enforce high standards for data governance, quality, lineage, and compliance”
  • Showcase your ability to evaluate and implement emerging data technologies through proof-of-concept projects
    — “Evaluate emerging technologies and lead POCs that shape the future of our data stack.”
  • Demonstrate your ability to work with structured and semi-structured data at scale using Delta, Parquet, etc.
    — “Experience working with structured and semi-structured data (e.g., Delta, JSON, Parquet, Avro) at scale.”
  • Include examples of how you have democratized data access for enterprise stakeholders in prior roles
    — “Previous experience Democratizing data at scale for the enterprise a huge plus.”
Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • designing scalable data pipelines
  • enhancing data flow
Typical background
computer sciencedata engineering

Skills & requirements

Required

ETLSQLPythonApache SparkData GovernanceCloud Platforms

Preferred

AIMachine LearningBig Data

Stack & domain

PythonSQLApache SparkDatabricksSnowflakeRedshiftBigQueryData OrchestrationData GovernanceData SecurityData PrivacyData ComplianceData ModelingData WorkflowsData DemocratizationData EngineeringData AnalysisData ScienceData PipelinesETLRJSONParquetAvroSalesforceNetsuiteAnaplanCommunicationTeamworkProblem-solvingLeadershipCuriosityEthicsAccountabilityFinanceEngineeringTechnology

About the role

Original posting from MeridianLink via Ashby

The Senior Data Engineer will design, build, implement, and maintain data processing pipelines for the extraction, transformation, and loading (ETL) of data from a variety of data sources. The role will develop robust and scalable solutions that transform data into a useful format for analysis, enhance data flow, and enable end-users to consume and analyze data faster and easier.

Expected Duties:

  • Senior Data Engineers will design, build, implement, and maintain data processing pipelines for the extraction, transformation, and loading (ETL) of data from a variety of data sources
  • Expected to lead the writing of complex SQL queries to support analytics needs
  • Responsible for developing technical tools and programming that leverage artificial intelligence, machine learning, and big-data techniques to cleanse, organize and transform data and to maintain, defend and update data structures and integrity on an automated basis
  • Senior Data Engineers will evaluate and recommend tools and technologies for data infrastructure and processing. Collaborate with engineers, data scientists, data analysts, product teams, and other stakeholders to translate business requirements to technical specifications and coded data pipelines
  • The role will work with tools, languages, data processing frameworks, and databases such as R, Python, SQL, Databricks, Spark, Delta, APIs. Work with structured and unstructured data from a variety of data stores, such as data lakes, relational database management systems, and/or data warehouses

Qualifications: Knowledge, Skills, and Abilities:

The role will include work on problems of diverse scopes where analysis of information requires evaluation of identifiable factors. Work is expected to be done independently through independent judgment.

  • Ability to assess unusual circumstances and use sophisticated analytical and problem-solving techniques to identify the cause
  • Working with people, processes facing Enterprise a plus, improvement of processes/reduction of costs in the enterprise processes leveraging data is preferable.
  • Ability to enhance relationships and networks with senior internal partners who are not familiar with the subject often requires persuasion
  • Architect and scale our modern data platform to support real-time and batch processing for financial forecasting, risk analytics, and customer insights
  • Enforce high standards for data governance, quality, lineage, and compliance
  • Partner with stakeholders across engineering, finance, sales, and compliance to translate business requirements into reliable data models and workflows.
  • Evaluate emerging technologies and lead POCs that shape the future of our data stack.
  • Champion a culture of security, automation, and continuous delivery in all data workflows

Technical Qualifications:

  • Deep expertise in Python, SQL, and distributed processing frameworks like Apache Spark, Databricks, Snowflake, Redshift, BigQuery.
  • Proven experience with cloud-based data platforms (preferably AWS or Azure).
  • Hands-on experience with data orchestration tools (e.g., Airflow, dbt) and data warehouses (e.g., Databricks, Snowflake, Redshift, BigQuery).
  • Strong understanding of data security, privacy, and compliance within a financial services context.
  • Experience working with structured and semi-structured data (e.g., Delta, JSON, Parquet, Avro) at scale.
  • Familiarity with modelling datasets in Salesforce, Netsuite and Anaplan to solve business use cases required.
  • Previous experience Democratizing data at scale for the enterprise a huge plus.

Educational Qualifications and Work experience

  • Bachelor's or master's degree in computer science, Engineering, or a related field.
  • 6-8 years of experience in data engineering, with a strong focus on financial systems on SaaS platforms.

Source: MeridianLink careers (Ashby)

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