About the role

We are seeking an accomplished Data Engineer to join our dynamic and rapidly expanding Tech Ops division. With significant projected growth, this is an opportunity to drive meaningful technical impact. In this role, you will work directly with customers, data vendors, and internal engineering teams to design, implement, and optimize complex data integration solutions within a modern, large‑scale cloud environment.

You will leverage advanced skills in distributed computing, data architecture, and cloud-native engineering to enable scalable, resilient, and high‑performance data ingestion and transformation pipelines. As a trusted technical advisor, you will guide customers in adopting Abacus’s core data management platform and ensure high-quality, compliant data operations across the lifecycle.

Your day to day

• Architect, design, and implement high-volume batch and real-time data pipelines using PySpark, SparkSQL, Databricks Workflows, and distributed processing frameworks.
• Build end‑to‑end ingestion frameworks integrating with Databricks, Snowflake, AWS services (S3, SQS, Lambda), and vendor data APIs, ensuring data quality, lineage, and schema evolution.
• Develop data modeling frameworks, including star/snowflake schemas and optimization techniques for analytical workloads on cloud data warehouses.
• Lead technical solution design for health plan clients, creating highly available, fault-tolerant architectures across multi-account AWS environments.
• Translate complex business requirements into detailed technical specifications, engineering artifacts, and reusable components.
• Implement security automation, including RBAC, encryption at rest/in transit, PHI handling, tokenization, auditing, and compliance with HIPAA and SOC 2 frameworks.
• Establish and enforce data engineering best practices, such as CI/CD for data pipelines, code versioning, automated testing, orchestration, logging, and observability patterns.
• Conduct performance profiling and optimize compute costs, cluster configurations, partitions, indexing, and caching strategies across Databricks and Snowflake environments.
• Produce high-quality technical documentation including runbooks, architecture diagrams, and operational standards.
• Mentor junior engineers through technical reviews, coaching, and training sessions for both internal teams and clients.

What you bring to the team

• Bachelor’s degree in Computer Science, Computer Engineering, or a closely related technical field.
• 7+ years of hands‑on experience as a Data Engineer working with large‑scale, distributed data processing systems in modern cloud environments.
• Working knowledge of U.S. healthcare data domains—including claims, eligibility, and provider datasets—and experience applying this knowledge to complex ingestion and transformation workflows.
• Strong ability to communicate complex technical concepts clearly across both technical and non‑technical stakeholders.
• Expert‑level proficiency in Python, SQL, and PySpark, including developing distributed data transformations and performance‑optimized queries.
• Demonstrated experience designing, building, and operating production‑grade ETL/ELT pipelines using Databricks, Airflow, or similar orchestration and workflow automation tools.
• Proven experience architecting or operating large‑scale data platforms using dbt, Kafka, Delta Lake, and event‑driven/streaming architectures, within a cloud‑native data services or platform engineering environment—requiring specialized knowledge of distributed systems, scalable data pipelines, and cloud‑scale data processing.
• Experience working with structured and semi‑structured data formats such as Parquet, ORC, JSON, and Avro, including schema evolution and optimization techniques.
• Strong working knowledge of AWS data ecosystem components—including S3, SQS, Lambda, Glue, IAM—or equivalent cloud technologies supporting high‑volume data engineering workloads.
• Proficiency with Terraform, infrastructure‑as‑code methodologies, and modern CI/CD pipelines (e.g., GitLab) supporting automated deployment and versioning of data systems.
• Deep expertise in SQL and compute optimization strategies, including Z‑Ordering, clustering, partitioning, pruning, and caching for large‑scale analytical and operational workloads.
• Hands‑on experience with major cloud data warehouse platforms such as Snowflake (preferred), BigQuery, or Redshift, including performance tuning and data modeling for analytical environments.

What we would like to see but not required:

• Experience in large-scale healthcare or payer data environments.

What you’ll get in return :

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements :

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST

About the role

We are seeking an accomplished Data Engineer to join our dynamic and rapidly expanding Tech Ops division. With significant projected growth, this is an opportunity to drive meaningful technical impact. In this role, you will work directly with customers, data vendors, and internal engineering teams to design, implement, and optimize complex data integration solutions within a modern, large‑scale cloud environment.

You will leverage advanced skills in distributed computing, data architecture, and cloud-native engineering to enable scalable, resilient, and high‑performance data ingestion and transformation pipelines. As a trusted technical advisor, you will guide customers in adopting Abacus’s core data management platform and ensure high-quality, compliant data operations across the lifecycle.

Your day to day :

• Architect, design, and implement high-volume batch and real-time data pipelines using PySpark, SparkSQL, Databricks Workflows, and distributed processing frameworks.
• Build end‑to‑end ingestion frameworks integrating with Databricks, Snowflake, AWS services (S3, SQS, Lambda), and vendor data APIs, ensuring data quality, lineage, and schema evolution.
• Develop data modeling frameworks, including star/snowflake schemas and optimization techniques for analytical workloads on cloud data warehouses.
• Lead technical solution design for health plan clients, creating highly available, fault-tolerant architectures across multi-account AWS environments.
• Translate complex business requirements into detailed technical specifications, engineering artifacts, and reusable components.
• Implement security automation, including RBAC, encryption at rest/in transit, PHI handling, tokenization, auditing, and compliance with HIPAA and SOC 2 frameworks.
• Establish and enforce data engineering best practices, such as CI/CD for data pipelines, code versioning, automated testing, orchestration, logging, and observability patterns.
• Conduct performance profiling and optimize compute costs, cluster configurations, partitions, indexing, and caching strategies across Databricks and Snowflake environments.
• Produce high-quality technical documentation including runbooks, architecture diagrams, and operational standards.
• Mentor junior engineers through technical reviews, coaching, and training sessions for both internal teams and clients.

What you bring to the team :

• Bachelor’s degree in Computer Science, Computer Engineering, or a closely related technical field.
• 5+ years of hands‑on experience as a Data Engineer working with large‑scale, distributed data processing systems in modern cloud environments.
• Working knowledge of U.S. healthcare data domains—including claims, eligibility, and provider datasets—and experience applying this knowledge to complex ingestion and transformation workflows.
• Strong ability to communicate complex technical concepts clearly across both technical and non‑technical stakeholders.
• Expert‑level proficiency in Python, SQL, and PySpark, including developing distributed data transformations and performance‑optimized queries.
• Demonstrated experience designing, building, and operating production‑grade ETL/ELT pipelines using Databricks, Airflow, or similar orchestration and workflow automation tools.
• Proven experience architecting or operating large‑scale data platforms using dbt, Kafka, Delta Lake, and event‑driven/streaming architectures, within a cloud‑native data services or platform engineering environment—requiring specialized knowledge of distributed systems, scalable data pipelines, and cloud‑scale data processing.
• Experience working with structured and semi‑structured data formats such as Parquet, ORC, JSON, and Avro, including schema evolution and optimization techniques.
• Strong working knowledge of AWS data ecosystem components—including S3, SQS, Lambda, Glue, IAM—or equivalent cloud technologies supporting high‑volume data engineering workloads.
• Proficiency with Terraform, infrastructure‑as‑code methodologies, and modern CI/CD pipelines (e.g., GitLab) supporting automated deployment and versioning of data systems.
• Deep expertise in SQL and compute optimization strategies, including Z‑Ordering, clustering, partitioning, pruning, and caching for large‑scale analytical and operational workloads.
• Hands‑on experience with major cloud data warehouse platforms such as Snowflake (preferred), BigQuery, or Redshift, including performance tuning and data modeling for analytical environments.

What we would like to see but not required:

• Experience in large-scale healthcare or payer data environments.

What you’ll get in return :

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements :

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST

Strategic Project Lead / Delivery Manager 

Data Annotation & AI Delivery

8+ Years of Experience

Position Overview

We are looking for a seasoned Delivery Manager (8+ years of experience) to lead large-scale data annotation and curation projects in support of cutting-edge LLM and multimodal AI research. You will oversee end-to-end delivery for projects with 100+ team members, ensure world-class data quality, and drive process and tooling innovation in collaboration with AI researchers and cross-functional teams.

This role is ideal for someone who combines deep technical expertise, proven leadership in managing large teams, and a passion for advancing AI/ML innovation. Given that our key customers operate in the data analytics, data engineering, and Business Intelligence (BI) space, the ideal candidate will bring hands-on leadership experience with platforms such as Databricks and Snowflake, enabling them to deeply understand customer domains, speak their language, and deliver annotation outcomes that are contextually relevant and technically sound.

Primary Responsibilities

1. Team Leadership & Development

• Lead, mentor, and grow large-scale teams of annotators and trainers.
• Drive performance management, career development, and targeted training.
• Foster a culture of excellence, ownership, and continuous learning.
• Design and implement motivation strategies (including gamification) to boost engagement and reward top performers.

1. Project Management & Coordination

• Define and manage project scope, objectives, timelines, and quality metrics.
• Align with cross-functional stakeholders on goals and deliverables.
• Proactively identify and resolve delivery risks, blockers, and dependencies.
• Provide clear and consistent reporting on progress, challenges, and solutions.

1. Data Quality & Integrity

• Own quality control and data integrity across the annotation lifecycle.
• Analyze datasets to identify trends, anomalies, and insights for improvement.
• Implement best practices for annotation, evaluation, and data curation.
• Share learnings and continuously improve quality processes.

1. Technical Collaboration & Innovation

• Partner with engineering and research teams to optimize internal tooling.
• Stay ahead of emerging trends in AI, ML, and multimodal processing.
• Champion innovations that reduce task completion times and improve efficiency.
• Maintain clear and scalable documentation for processes and training.

1. Customer & Domain Engagement

• Serve as a domain-knowledgeable point of contact for customers operating in data engineering, analytics, and BI environments.
• Leverage hands-on experience with platforms like Databricks and Snowflake to understand customer data workflows, terminology, and quality expectations.
• Translate customer-specific data engineering and BI contexts into clear, actionable annotation guidelines and quality benchmarks for delivery teams.
• Collaborate with customers to define annotation taxonomies and evaluation criteria that align with their data pipeline and analytics use cases.

Required Skills & Qualifications

• 10+ years of experience in engineering, program management, or delivery of large-scale projects.
• Proven ability to lead and scale large teams (100+ members) with measurable impact.
• Strong technical grounding in AI/ML, data annotation, and multimodal systems.
• Hands-on leadership experience in data analytics, data engineering, or BI domains, with working knowledge of platforms such as Databricks and/or Snowflake (architecture, pipelines, data modeling, and production workflows).
• Familiarity with the modern data stack including ETL/ELT pipelines, data warehouses, data lakes, and BI tools (Power BI, Tableau, Looker, or similar).
• Ability to translate complex data engineering and analytics concepts into annotation frameworks, quality standards, and training materials.
• Proficiency in Python (preferred) or Swift for data analysis and automation.
• Strong mathematical and analytical skills for data-driven problem solving.
• Experience in data curation, annotation pipelines, and quality control.
• Excellent communication and stakeholder management skills.
• Track record of driving high-quality outputs under tight deadlines.

Preferred Skills & Qualifications

• Advanced degree in Computer Science, Data Science, AI, or related fields.
• Experience applying gamification to workforce engagement.
• Familiarity with modern project management tools & methodologies.
• Exposure to state-of-the-art multimodal AI technologies.
• Prior experience managing annotation or data labeling projects for customers in the data engineering, analytics, or BI space.
• Familiarity with SQL at an advanced level and data orchestration tools such as Apache Airflow, dbt, or Prefect.
• Certifications in Databricks (e.g., Data Engineer Associate) or Snowflake (e.g., SnowPro Core) are a plus.

About the Role

We are looking for a Data Modeler to join a global data modeling team responsible for transforming business requirements into conceptual and logical data models. This role requires understanding of healthcare data domains (Claims, Membership, Enrollment, Provider, Billing, Interoperability - FHIR, HL7, ADT, CCDA, etc.) and experience in enterprise data modeling best practices.

The Data Modeler will work closely with the Director of Data Modeling and Senior Data Modelers to adhere to data modeling standards, metadata governance, and follow data architecture strategies that align with business and technical needs. They will also collaborate with business analysts, product analysts, client implementation teams, and data governance teams to ensure models support operational and analytical use cases, high performance, and data integrity.

This is an exciting opportunity for a data modeler with experience in Databricks, Snowflake, schema evolution, and modern data engineering practices to shape the future of healthcare data platforms.

Your day to day

Identify and define requirements for canonical data models as well as consumption use cases required by enterprise healthcare customers. Your activities will include:

• Perform data profiling analysis.
• Use SQL, data modeling and data integrity principles.
• Build conceptual and logical data models aligned with enterprise architecture, business requirements and industry standards.
• Follow data modeling best practices, including schema evolution, lossless data modeling, metadata management, and data versioning.
• Use enterprise data modeling tools (Erwin, ER/Studio, DBT, or similar).
• Ensure models support analytical (data science, actuarial, reporting) and operational (Claims, Care Management) use cases.
• Maintain metadata, business glossary, entity relationship diagrams and data dictionaries to support data lineage and governance.
• Partner with Client Management, Product, and Business Analyst teams to understand client needs, assess data model impact, and ensure data models and related artifacts meet or exceed expectations.
• Work with client management teams to provide support and data model expertise during client implementation.

What you bring to the team

• 2+ years of experience in data modeling, enterprise data architecture, and cloud-based data engineering.
• Expertise in enterprise data modeling tools (Erwin, ER/Studio, DBSchema, or similar).
• NoSQL data model experience.
• Strong hands-on experience with cloud data platforms (Databricks, Snowflake) and data lakehouse architectures.
• Deep understanding of healthcare data models across Core Payer (Claims, Membership, Enrollment, Provider, Billing, Premium) and Clinical Interoperability (FHIR, HL7, ADT, CCDA, etc.) domains.
• Experience with schema evolution, lossless data modeling, and data versioning strategies.
• Strong background in SQL, data profiling, and metadata creation and maintenance.
• Ability to learn from senior modelers, enforce modeling best practices, and drive adoption of enterprise data standards.
• Excellent collaboration skills to work with data architects, engineers, and client implementation teams.

What we would like to see, but not required

• Healthcare reporting experience.
• Experience with OBT, data lakehouse, and metadata management frameworks.
• Familiarity with business intelligence (BI) and reporting solutions on modern cloud data warehouses.
• Knowledge of APIs, SDKs, and data distribution frameworks for enabling real-time and batch data access.
• Experience with data governance, security, and regulatory compliance in healthcare data management.

What you’ll get in return 

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements 

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST

Overview of Role

We are seeking a highly skilled Cloud Data Engineer to join our Managed Services team. In this role, you will be the technical backbone for our enterprise clients, responsible for designing, building, and maintaining high-performance data platforms using Snowflake and Azure Databricks. You will act as a trusted advisor, ensuring that our customers' data ecosystems are scalable, secure, and cost-optimized across multi-cloud environments.

Key Responsibilities

• Platform Engineering: Design and implement end-to-end data lakehouse architectures using Azure Databricks (Delta Lake),Azure HDInsights and Snowflake Cloud Data Warehouse.
• Pipeline Development: Build scalable, metadata-driven ETL/ELT pipelines using Azure Data Factory (ADF), PySpark, and SnowSQL, including ingestion workflows from HDInsight-managed Hadoop and Kafka clusters.
• Managed Services Support: Provide Tier-3 technical support for client environments, including performance tuning, complex bug fixes, and architectural optimizations.
• Multi-Cloud Integration: Oversee data movement and integration strategies across Azure, AWS, and GCP to ensure seamless "Data Everywhere" capabilities for clients.
• Cost & Performance Governance: Monitor and optimize Snowflake credit consumption and Databricks cluster usage to ensure clients receive maximum ROI.
• Security & Compliance: Implement enterprise-grade security including Role-Based Access Control (RBAC), Row-Level Security (RLS), and data masking (Dynamic Data Masking in Snowflake / Unity Catalog in Databricks).

Required Technical Skillset

Experience & Qualifications

• Experience: 5+ years in Data Engineering, with at least 3 years of hands-on experience in both Snowflake and Databricks.
• Managed Services Background: Proven experience supporting external customers with strict SLAs and complex governance requirements.
• Multi-Cloud (Plus): Direct experience deploying Snowflake or Databricks on AWS or GCP is highly desirable.
• Certifications (Preferred): * SnowPro Core or Advanced Architect
• Databricks Certified Data Engineer Professional
• Microsoft Certified: Azure Data Engineer Associate (DP-203)

What’s in it for you?

Opportunity To Unlock Your Creativity
Think of all the times you were held back from trying new ideas because you were boxed in by bureaucratic legacy processes or old school tactics. Having a growth mindset is deeply ingrained into our company culture since day 1 so Fictiv is an environment where you have the creative liberty and support of the team to try big bold ideas to achieve our sales and customer goals.

Opportunity To Grow Your Career
At Fictiv, you'll be surrounded by supportive teammates who will push you to be your best through their curiosity and passion.

Impact In This Role

The Data Intelligence team creates reports and dashboards to provide meaningful insights from the data warehouse. This team works closely with cross-functional stakeholders and leverages multiple Business Intelligence (BI) technologies to enable data-driven decision-making across the organization.
As a Senior Data Engineer on the Data Intelligence team, you will play a key role in designing, managing, and evolving Fictiv’s reporting data warehouse and semantic layer. You will partner closely with the Data Intelligence Manager, Data Engineers, Data Analysts, and Product Managers to ensure replicated data from production systems is transformed into scalable, trusted semantic data models that power consistent, accurate self-service reporting. Acting as a bridge between business and technology, you will translate analytical requirements into well-structured data models and transformations that enable reliable dashboards and insights across multiple domains.
In addition, you will collaborate with Infrastructure, Software, and Systems Engineering teams to strengthen database design and query efficiency across the broader data ecosystem. You will provide hands-on guidance on optimal table structures, indexing and clustering approaches, and SQL query patterns that improve performance and scalability. By proactively identifying bottlenecks and recommending architectural and modeling improvements, you will help ensure that both analytical and operational workloads are efficient, maintainable, and aligned with best practices.
This is a hands-on role responsible for leading the design, transformation, implementation, and operation of data solutions across disparate source systems—driving best practices in data modeling, semantic layer design, performance optimization, documentation, and governance. You will plan, design, and operate data pipelines and models using Agile methodologies to deliver high-quality, maintainable, and secure data assets

You will report to the Data Analytics Manager.

What You’ll Be Doing

• Design efficient, scalable, and maintainable semantic data models that align with business logic and enable intuitive self-service analytics and reporting across multiple domains.
  
• Build, maintain, and optimize data transformations and pipelines that transform replicated production data into trusted, analytics-ready models.
  
• Collaborate with stakeholders across data, engineering, product, and business teams to resolve data-related issues, clarify requirements, and align on solution design.
  
• Provide technical leadership and guidance to Software Engineers and Data Engineers on query performance, optimization techniques, and efficient data access patterns.
  
• Drive ongoing improvement of database and table structures, partnering with engineers to validate and refine table design, joins, partitioning/clustering, and other performance-related considerations.
  
• Review and advise on SQL query design for both application and analytics use cases, helping teams design queries that are performant, maintainable, and cost-effective.
  
• Analyze performance metrics and execution plans to identify bottlenecks in data models, queries, and transformations, and recommend practical tuning and refactoring opportunities.
  
• Follow and contribute to SQL coding standards, data modeling best practices, change control procedures, and data warehouse governance processes.
  
• Develop and maintain data flow diagrams, data dictionaries, and schema documentation with a focus on clarity, consistency, and enterprise data management.
  
• Understand and document end-to-end data flows and dependencies from production systems through the ELT pipeline into Snowflake and downstream BI tools, supporting delivery, release, and change management.
  
• Execute and lead data design sessions and technical reviews to gather requirements, validate modeling approaches, and communicate design decisions and trade-offs to stakeholders.
  
• Research emerging technologies, patterns, and tools related to Snowflake, dbt, SQL optimization, and semantic modeling, and recommend enhancements to Fictiv’s data platform and practices.
  
• Help set broad direction and vision for the reporting data platform architecture, influencing data engineering, analytics, and application data design strategy.
  
• Partner with internal and external teams for knowledge transfer and smooth handoff in advance of new feature launches or reporting changes.
  
• Work effectively with remote and cross-functional teams across time zones, fostering strong communication and collaboration.
  
• Proven experience working with large and complex datasets and comfort writing advanced SQL queries or leveraging BI tools for analysis and transformation.
  
• Deep understanding of data and analytics architecture, including data warehousing concepts, semantic data modeling, and data integration best practices, with a strong focus on Snowflake and ELT pipelines.
  
• Strong knowledge of SQL and BI concepts, including dimensional modeling, data warehouse design, and dashboarding; ability to translate analytical needs into robust semantic models.
  
• Demonstrated ability to guide and influence database and schema optimization, including improving query performance, advising on efficient access patterns, and ensuring well-structured data that support scalable workloads.
  
• Experience mentoring and guiding Data Engineers and Software Engineers to develop robust, scalable, and maintainable data models, queries, and database structures.
  
• Ability to design and maintain data transformation and workload management processes, including job orchestration, dependency handling, scheduling, and metadata management.
  
• Excellent analytical skills and experience working with both structured and semi-structured data, from exploration through to production-ready models.
  
• Ability to evaluate new tools, database features, and methodologies relative to the existing data stack and propose detailed, pragmatic recommendations for improvement.
  
• Strong ability to perform root cause analysis on data quality, performance, and process issues to identify inefficiencies, ensure data integrity, and deliver actionable insights.
  
• Clear and effective communicator—capable of providing business context for engineers and technical context for non-engineers, especially around trade-offs in model and schema design.
  
• Confident presenting to audiences with mixed levels of technical understanding and experience, including walkthroughs of models, schemas, and query patterns.
  
• Curious, resourceful, and adaptable; comfortable navigating ambiguity, learning new technologies (e.g., new Snowflake/dbt features), and evolving processes to improve outcomes.

Desired Traits

• B.S. in Data Science, Computer Science, Information Systems, or a related field.
  
• 15+ years of experience as a Data Engineer, designing and maintaining large-scale data systems, building scalable data models, and delivering analytical solutions through modern BI and data pipeline tools.
  
• 10+ years of experience working with modern cloud and on-premise database platforms—such as Snowflake, AWS Redshift, Azure Synapse, Google BigQuery, or traditional RDBMS systems (e.g., PostgreSQL, MySQL, SQL Server, Oracle)—with a strong focus on schema design, query performance, execution plan analysis, and scalable data access patterns, rather than day-to-day operational administration.
  
• 8+ years of experience developing and maintaining data transformation workflows using dbt or a similar tool, with expertise in model design, testing, documentation, environment management, deployment automation, and enforcing version control and development best practices.
  
• Demonstrated expertise working with complex datasets and advanced SQL, including multi-level CTEs, window functions, and recursive queries, with an emphasis on performance and readability.
  
• Experience with BI platforms such as Heap, Sigma Computing, or similar technologies (e.g., Tableau, Looker, or QuickSight), and how they interact with warehouse models.
  
• Strong knowledge of BI and analytical concepts, including regressions, recursion, sliding window functions, financial metrics, marketing funnel metrics, and logistics and fulfillment analytics.
  
• Experience building and maintaining processes that support data transformation, data structures, metadata management, dependency handling, and workload optimization in a modern ELT/warehouse environment.
  
• Proven ability to lead and coordinate cross-functional initiatives, including partnering with Software Engineers to refine schemas and queries, conducting interviews, and performing analyses to develop business cases for projects and improvements.
  
• Experience delivering training sessions and technical enablement for internal and external stakeholders, especially around data models, schemas, and query best practices during system and process rollouts.
  
• Excellent communication and collaboration skills, with the ability to translate complex technical concepts (e.g., query plans, indexing/clustering trade-offs, modeling decisions) for non-technical audiences.
  
• Demonstrated leadership and mentoring abilities, fostering technical excellence and knowledge sharing across data and engineering teams, particularly in areas of semantic modeling, SQL craftsmanship, and performance optimization.

Perks and Benefits

• Medicliam
• Annual Paid holidays
• Gratuity
• Provident Fund 
  And much, much more!

Interested in learning more?  We look forward to hearing from you soon.

Summary:

The Senior Business Intelligence Analyst plays a pivotal role in driving data-driven decision-making across the organization. This role is responsible for developing dynamic dashboards, delivering actionable insights, and ensuring the effective use of analytics tools to support strategic objectives.

Responsibilities:

• Develop, maintain, and enhance Amazon Quicksight dashboards and reports to provide visibility into key business metrics.
• Analyze GTM data to deliver insights that improve decision-making and drive business outcomes.
• Create robust data models to streamline reporting and ensure alignment with strategic objectives.
• Design, implement, and manage weekly, monthly, and quarterly scorecards and forecasts for leadership.
• Partner with Sales, Marketing, and Customer Experience teams to ensure reporting aligns with business needs.
• Lead the administration and enhancement of Quicksight and other BI platforms to ensure scalability and usability.
• Provide provocative presentations to key stakeholders to advance understanding and strategy through compelling visuals.
• Educate GTM teams on how to use BI tools effectively, fostering a data-driven culture.
• Develop standardized reports and packages that provide field teams with the insights they need to succeed.
• Respond to requests for deep-dive analyses to uncover trends, opportunities, or risks.

Required Skills & Experience:

• 4+ years in Business Intelligence, Data Analytics, or a related field, with hands-on expertise in AWS Quicksight.
• Proficiency in SQL, Python, dbt, data modeling, data visualization, and advanced Excel/Google Sheets for modeling and analysis.
• Experience with AWS Quicksight/Athena/S3/Lambda, Gitlab, VSCode, Salesforce CRM, Google Sheets, Google Slides.
• Preferred experience with Fivetran.
• Strong ability to interpret data, identify trends, and translate findings into actionable strategies.
• Exceptional attention to detail and a knack for solving complex business problems.
• Ability to clearly convey insights and recommendations to both technical and non-technical stakeholders.
• Bachelor’s degree in Data Science, Computer Science, Analytics, or a relevant/equivalent field.
• Working knowledge of SaaS metrics (Bookings, Pipeline, Churn, NRR, GRR, LTV, ARR, CAC, etc.) and Revenue Forecasting.
• Bonus points for experience with other BI tools (e.g., Tableau, Power BI) and domain expertise in revenue enablement, GTM/sales operations, professional services reporting, customer intelligence, or churn prediction models.

About Us

HG Insights is the global leader in technology intelligence, delivering actionable AI driven insights through advanced data science and scalable big data solutions. Our platform informs go-to-market decisions, and we influence how businesses spend millions of marketing and sales budgets.

What You’ll Do:

• Design, build, and optimize large-scale distributed data pipelines for processing billions of events from and to multiple sources of data.
• Architect and scale enterprise-grade big-data systems, including data lakes, ETL/ELT workflows, and syndication platforms for customer-facing products.
• Orchestrate pipelines and workflows with Airflow.
• Write optimized data into analytical databases such as ClickHouse, DuckDB, and Redshift
• Ensure data quality, consistency, and reliability across the pipeline.
• Monitor pipeline performance and troubleshoot data issues.
• Collaborate with product teams to develop features across databases, and backend services.
• Implement cutting-edge solutions for data ingestion, transformation, and analytics.
• Drive system reliability through automation, CI/CD pipelines (Docker, Kubernetes, Terraform), and infrastructure-as-code practices.

What You’ll Be Responsible For

• Development of the Data side of our Platform, ensuring scalability, performance, and cost-efficiency across distributed systems.
• Collaborating in agile workflows (daily stand-ups, sprint planning) to deliver features rapidly while maintaining system stability.
• Ensuring security and compliance across data workflows, including access controls, encryption, and governance policies.

What You’ll Need

• BS/MS/Ph.D. in Computer Science or related field, with 7+ years of experience building production-grade big data systems.
• Strong SQL skills and solid data modeling fundamentals with hands-on experience building ETL / ELT pipelines.
• Proficiency in Python for data processing and integrations.
• Familiarity with dbt, Airflow, Databricks and modern analytics engineering practices

Nice-to-Haves

• Knowledge of data governance frameworks and compliance standards (GDPR, CCPA).
• Contributions to open-source big data projects or published technical blogs/papers.
• DevOps proficiency in monitoring tools (Prometheus, Grafana) and serverless architectures.

What You Will Do and Learn

As a Data Engineer in Data Solutions, you’ll be responsible for designing, implementing, and testing proposed modern analytic solutions.  Working closely with our client partners and architects, you’ll develop relationships with key technical resources while delivering tangible business outcomes. 

Qualifications:

· 2-4 years’ experience developing industry leading business intelligence and analytic solutions

· Must have thorough knowledge of data warehouse concepts and dimensional modelling

· Must have experience in writing advanced SQL

· Must have at least 2+ years of hands-on experience on DBT (Data Build Tool). Mandatory to have most recent hands-on experience on DBT.

· Must have experience working with DBT on one or more of the modern databases like Snowflake / Amazon Redshift / BigQuery / Databricks / etc.

· Hands-on experience with Snowflake would carry higher weightage

· Snowflake SnowPro Core certification would carry higher weightage

· Experience working in AWS, Azure, GCP or similar cloud data platform would be an added advantage

· Hands-on experience on Azure would carry higher weightage

· Must have experience in setting up DBT projects

· Must have experience in understanding / creating / modifying & optimizing YML files within DBT

· Must have experience in implementing and managing data models using DBT, ensuring efficient and scalable data transformations

· Must have experience with various materialization techniques within DBT

· Must have experience in writing & executing DBT Test cases

· Must have experience in setting up DBT environments

· Must have experience in setting up DBT Jobs

· Must have experience with writing DBT Jinja and Macros

· Must have experience in creating DBT Snapshots

· Must have experience in creating & managing incremental models using DBT

· Must have experience with DBT Docs

· Should have a good understanding of DBT Seeds

· Must have experience with DBT Deployment

· Must Experience with architecting data pipelines using DBT, utilizing advanced DBT features

· Proficiency in version control systems and CI/CD

· Must have hands-on experience configuring DBT with one or more version control systems like Azure DevOps / Github / Gitlab / etc.

· Must have experience in PR approval workflow

· Participate in code reviews and best practices for SQL and DBT development

· Experience working with visualization tools such as Tableau, PowerBI, Looker and other similar analytic tools would be an added advantage

· 2+ years of Business Data Analyst experience

· 2+ years of experience writing Business requirements, Use cases and/or user stories, for data warehouse or data mart initiatives.

· Understanding and experience on ETL/ELT is an added advantage

· 2+ years of consulting experience working on project-based delivery using Software Development Life Cycle (SDLC)

· 2+ years of years of experience with relational databases (Postgres, MySQL, SQL Server, Oracle, Teradata etc.)

· 2+ years of experience creating functional test cases and supporting user acceptance testing

· 2+ years of experience in Agile/Kanban/DevOps Delivery

· Outstanding analytical, communication, and interpersonal skills

· Ability to manage projects and teams against planned work

· Responsible for managing the day-to-day client relationship on projects

· Manage the Data engineering lifecycle including research, proof of concepts, architecture, design, development, test, deployment, and maintenance

· Collaborate with team members to design and implement technology that aligns with client business objectives

· Build proof of concepts for a modern analytics stack supporting a variety of Cloud-based Business Systems for potential clients

· Team management experience and ability to manage, mentor and develop talent of assigned junior resources

· Create actionable recommendations based on identified platform, structural and/or logic problems

· Communicate and demonstrate a clear understanding of client business needs, goals, and objectives

· Collaborate with other architects on solution designs and recommendations.