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Director - Data engineering

What are we looking for

real solver?

Solver? Absolutely. But not the usual kind. We're searching for the architects of the audacious & the pioneers of the possible. If you're the type to dismantle assumptions, re-engineer ‘best practices,’ and build solutions that make the future possible NOW, then you're speaking our language.

Your Responsibilities

what you will wake up to solve.

1. Delivery & Tactical Rigor

• Methodology Implementation: Implement and manage a unified, 'DataOps-First' methodology for data engineering delivery (ETL/ELT pipelines, Data Modeling, MLOps, Data Governance) within assigned business units. This ensures predictable outcomes and trusted data integrity by reducing architecture variability at the project level.
• Operational Stewardship: Drive initiatives to optimize team utilization and enhance operational efficiency within the practice. You manage the commercial success of your squads, ensuring data delivery models (from migration to modern data stack implementation) are executed profitably, scalably, and cost-effectively.
• Execution & Technical Resolution
• Technical Escalation: Serve as the primary escalation point for delivery issues, personally leading the resolution of complex data integration bottlenecks and pipeline failures to protect client timelines and data reliability standards.
• Quality Enforcement
• Quality Oversight: Execute and monitor technical data quality standards, ensuring engineering teams adhere to strict policies regarding data lineage, automated quality checks (observability), security/privacy compliance (GDPR/CCPA/PII), and active catalog management.

2. Strategic Growth & Practice Scaling

• Talent & Scaling Execution: Execute the strategy for data engineering talent acquisition and development within your business units. Implement objective metrics to assess and grow the 'Data-Native' DNA of your teams, ensuring squads are consistently equipped to handle petabyte-scale environments and high-impact delivery.
• Offerings Alignment: Drive the adoption of standardized regional offerings (e.g., Modern Data Platform, Data Mesh, Lakehouse Implementation). Ensure your teams leverage the profitable frameworks defined by the practice to accelerate time-to-insight and eliminate architectural fragmentation in client environments.
• Innovation & IP Development: Lead the practical integration of Vector Databases and LLM-ready architectures into project delivery. Champion the hands-on development of IP and reusable accelerators (e.g., automated ingestion engines) that improve delivery speed and enhance data availability across your portfolio.

3. Leadership & Unit Management

• Unit Leadership: Directly lead, mentor, and manage the Engineering Managers and Lead Architects within your business unit. Hold your teams accountable for project-level operational consistency, technical talent development, and strict adherence to the practice's data governance standards.
• Stakeholder Communication: Clearly articulate the business unit’s operational performance, technical quality metrics, and delivery progress to the C-suite Stakeholders and regional client leadership, bridging the gap between technical execution and business value.
• Ecosystem Alignment: Maintain strong technical relationships with key partner contacts (Snowflake, Databricks, AWS/GCP). Align team delivery capabilities with current product roadmaps and ensure squad-level participation in training, certifications, and partner-led enablement opportunities.

Welcome to Searce

The ‘process-first’, AI-native modern tech consultancy that's rewriting the rules.

We don’t do traditional.

As an engineering-led consultancy, we are dedicated to relentlessly improving the real business outcomes. Our solvers co-innovate with clients to futurify operations and make processes smarter, faster & better.

Functional Skills

1. Delivery Management & Operational Excellence

• Methodology Execution: Expert capability in implementing and enforcing a unified delivery methodology (DataOps, Agile, Mesh Principles) within specific business units. Proven track record of auditing squad-level adherence to ensure consistency across the project lifecycle.
• Operational Performance: High proficiency in managing day-to-day operational metrics, including squad utilization, resource forecasting, and productivity tracking. Skilled at optimizing team performance to meet profitability and efficiency targets.
• SOW & Risk Mitigation: Proven experience in operationalizing Statement of Work (SOW) requirements and identifying technical delivery risks early. Expert at mitigating scope creep and data-specific bottlenecks (e.g., latency, ingestion gaps) before they impact client outcomes.
• Technical Escalation Leadership: Demonstrated ability to lead "war room" efforts to resolve complex pipeline failures or data integrity issues. Skilled at providing clear, rapid remediation plans and communicating technical status directly to regional stakeholders.

2. Architectural Implementation & Technical Oversight

• Modern Stack Proficiency: Deep, hands-on expertise in implementing Cloud-Native architectures (Lakehouse, Data Mesh, MPP) on Snowflake, Databricks, or hyperscalers. Ability to conduct deep-dive architectural reviews and course-correct design decisions at the squad level to ensure scalability.
• Operationalizing Governance: Proven experience in embedding data quality and observability (completeness, freshness, accuracy) directly into the CI/CD pipeline. Responsible for technical enforcement of regulatory compliance (GDPR/PII) and maintaining the integrity of data catalogs across active projects.
• Applied Domain Expertise: Practical experience leading the delivery of high-growth solutions, specifically Generative AI infrastructure (RAG, Vector DBs), Real-Time Streaming, and large-scale platform migrations with a focus on zero-downtime execution.
• DataOps & Engineering Standards: Expert-level mastery of DataOps, including the setup and management of orchestration frameworks (Airflow, Dagster) and Infrastructure as Code (IaC). You ensure that automation is a baseline requirement, not an afterthought, for all delivery teams.

3. Unit Management & Commercial Execution

• Unit & Team Management: Proven success in leading and mentoring Engineering Managers and Lead Architects. Responsible for the operational metrics, technical output, and career development of the business unit's talent pool.
• Offerings Implementation & Scoping: Expertise in translating service offerings (e.g., Data Maturity Assessments, Lakehouse Builds) into accurate project scopes, technical estimates, and resource plans to ensure delivery is both profitable and competitive.
• Talent Growth & Mentorship: Functional ability to implement growth frameworks for data engineering roles. Focus on hands-on coaching and scaling high-performance technical talent to meet the demands of complex, petabyte-scale environments.
• Partner Enablement: Functional competence in managing regional technical relationships with major partners (Snowflake, Databricks, GCP/AWS). Drives squad-level certifications, joint technical enablement, and alignment with partner product roadmaps.

Tech Superpowers

• Modern Data Architect – Reimagines business with the Modern Data Stack (MDS) to deliver data mesh implementations, insights, & real value to clients.
• End-to-End Ecosystem Thinker – Builds modular, reusable data products across ingestion, transformation (ETL/ELT), governance, and consumption layers.
• Distributed Compute Savant – Crafts resilient, high-throughput architectures that survive petabyte-scale volume and data skew without breaking the bank.
• Governance & Integrity Guardian – Embeds data quality, complete lineage, and privacy-by-design (GDPR/PII) into every table, view, and pipeline.
• AI-Ready Orchestrator – Engineers pipelines that bridge structured data with Unstructured/Vector stores, powering RAG models and Generative AI workflows.
• Product-Minded Strategist – Balances architectural purity with time-to-insight; treats every dataset as a measurable "Data Product" with clear ROI.
• Pragmatic Stack Curator – Chooses the simplest tools that compound reliability; fluent in SQL, Python, Spark, dbt, and Cloud Warehouses.
• Builder @ Heart – Writes, reviews, and optimizes queries daily; proves architectures with cost-performance benchmarks, not slideware. Business-first, data-second, outcome focused technology leader.

Experience & Relevance

• Executive Experience: Minimum 10+ years of progressive experience in data engineering and analytics, with at least 3 years in a Senior Manager or Director -level role managing multiple technical teams and owning significant operational and efficiency metrics for a large data service line.
• Delivery Standardization: Demonstrated success in defining and implementing globally consistent, repeatable delivery methodologies (DataOps/Agile Data Warehousing) across diverse teams.
• Architectural Depth: Must retain deep, current expertise in Modern Data Stack architectures (Lakehouse, MPP, Mesh) and maintain the ability to personally validate high-level architectural and data pipeline design decisions.
• Operational Leadership: Proven expertise in managing and scaling large professional services organizations, demonstrated ability to optimize utilization, resource allocation, and operational expense.
• Domain Expertise: Strong background in Enterprise Data Platforms, Applied AI/ML, Generative AI integration, or large-scale Cloud Data Migration.
• Communication: Exceptional executive-level presentation and negotiation skills, particularly in communicating complex operational, data quality, and governance metrics to C-level stakeholders.

Join the ‘real solvers’

ready to futurify?

If you are excited by the possibilities of what an AI-native engineering-led, modern tech consultancy can do to futurify businesses, apply here and experience the ‘Art of the possible’. Don’t Just Send a Resume. Send a Statement.

Sr. Data Engineer (Data Warehouse-Snowflake)

Experience: 5+yrs

Location: Pune (Hybrid)

As a Senior Data engineer with Snowflake expertise you are a subject matter expert who is curious and an innovative thinker to mentor young professionals. You are a key person to convert Vision and Data Strategy for Data solutions and deliver them. With your knowledge you will help create data-driven thinking within the organization, not just within Data teams, but also in the wider stakeholder community.

Skills Preferred

• Advanced written, verbal, and analytic skills, and demonstrated ability to influence and facilitate sustained change. Ability to convey information clearly and concisely to all levels of staff and management about programs, services, best practices, strategies, and organizational mission and values.
• Proven ability to focus on priorities, strategies, and vision.
• Very Good understanding in Data Foundation initiatives, like Data Modelling, Data Quality Management, Data Governance, Data Maturity Assessments and Data Strategy in support of the key business stakeholders.
• Actively deliver the roll-out and embedding of Data Foundation initiatives in support of the key business programs advising on the technology and using leading market standard tools.
• Coordinate the change management process, incident management and problem management process.
• Ensure traceability of requirements from Data through testing and scope changes, to training and transition.
• Drive implementation efficiency and effectiveness across the pilots and future projects to minimize cost, increase speed of implementation and maximize value delivery

Knowledge Preferred

• Extensive knowledge and hands on experience with Snowflake and its different components like User/Group, Data Store/ Warehouse management, External Stage/table, working with semi structured data, Snowpipe etc.
• Implement and manage CI/CD for migrating and deploying codes to higher environments with Snowflake codes.
• Proven experience with Snowflake Access control and authentication, data security, data sharing, working with VS Code extension for snowflake, replication, and failover, optimizing SQL, analytical ability to troubleshoot and debug on development and production issues quickly is key for success in this role.
• Proven technology champion in working with relational, Data warehouses databases, query authoring (SQL) as well as working familiarity with a variety of databases. 
• Highly Experienced in building and optimizing complex queries. Good with manipulating, processing, and extracting value from large, disconnected datasets.
• Your experience in handling big data sets and big data technologies will be an asset.
• Proven champion with in-depth knowledge of any one of the scripting languages: Python, SQL, Pyspark.

Primary responsibilities

• You will be an asset in our team bringing deep technical skills and capabilities to become a key part of projects defining the data journey in our company, keen to engage, network and innovate in collaboration with company wide teams.
• Collaborate with the data and analytics team to develop and maintain a data model and data governance infrastructure using a range of different storage technologies that enables optimal data storage and sharing using advanced methods.
• Support the development of processes and standards for data mining, data modeling and data protection.
• Design and implement continuous process improvements for automating manual processes and optimizing data delivery.
• Assess and report on the unique data needs of key stakeholders and troubleshoot any data-related technical issues through to resolution.
• Work to improve data models that support business intelligence tools, improve data accessibility and foster data-driven decision making.
• Ensure traceability of requirements from Data through testing and scope changes, to training and transition.
• Manage and lead technical design and development activities for implementation of large-scale data solutions in Snowflake to support multiple use cases (transformation, reporting and analytics, data monetization, etc.).
• Translate advanced business data, integration and analytics problems into technical approaches that yield actionable recommendations, across multiple, diverse domains; communicate results and educate others through design and build of insightful presentations.
• Exhibit strong knowledge of the Snowflake ecosystem and can clearly articulate the value proposition of cloud modernization/transformation to a wide range of stakeholders.

Relevant work experience

Bachelors in a Science, Technology, Engineering, Mathematics or Computer Science discipline or equivalent with 7+ Years of experience in enterprise-wide data warehousing, governance, policies, procedures, and implementation.

Aptitude for working with data, interpreting results, business intelligence and analytic best practices.

Business understanding

Good knowledge and understanding of Consumer and industrial products sector and IoT. 

Good functional understanding of solutions supporting business processes.

Skill Must have

• Snowflake 5+ years
• Overall different Data warehousing techs 5+ years
• SQL 5+ years
• Data warehouse designing experience 3+ years
• Experience with cloud and on-prem hybrid models in data architecture
• Knowledge of Data Governance and strong understanding of data lineage and data quality
• Programming & Scripting: Python, Pyspark
• Database technologies such as Traditional RDBMS (MS SQL Server, Oracle, MySQL, PostgreSQL)

Nice to have

• Demonstrated experience in modern enterprise data integration platforms such as Informatica
• AWS cloud services: S3, Lambda, Glue and Kinesis and API Gateway, EC2, EMR, RDS, Redshift and Kinesis
• Good understanding of Data Architecture approaches
• Experience in designing and building streaming data ingestion, analysis and processing pipelines using Kafka, Kafka Streams, Spark Streaming, Stream sets and similar cloud native technologies.
• Experience with implementation of operations concerns for a data platform such as monitoring, security, and scalability
• Experience working in DevOps, Agile, Scrum, Continuous Delivery and/or Rapid Application Development environments
• Building mock and proof-of-concepts across different capabilities/tool sets exposure
• Experience working with structured, semi-structured, and unstructured data, extracting information, and identifying linkages across disparate data sets

Enterprise Data Architect - Dataeconomy (25+ Years Experience)

About Dataeconomy:

Dataeconomy is a rapidly growing company at the forefront of Information Technology. We are driven by data and committed to using it to make better decisions, improve our products, and deliver exceptional value to our customers.

Job Summary:

Dataeconomy seeks a seasoned and strategic Enterprise Data Architect to lead the company's data transformation journey. With 25+ years of experience in data architecture and leadership, you will be pivotal in shaping our data infrastructure, governance, and culture. You will leverage your extensive expertise to build a foundation for future growth and innovation, ensuring our data assets are aligned with business objectives and drive measurable value.

Responsibilities:

Strategic Vision and Leadership:

Lead the creation and execution of a long-term data strategy aligned with the company's overall vision and goals.

Champion a data-driven culture across the organization, fostering cross-functional collaboration and data literacy.

Advise senior leadership on strategic data initiatives and their impact on business performance.

Architecture and Modernization:

Evaluate and modernize the existing data architecture, recommending and implementing innovative solutions.

Design and implement a scalable data lake/warehouse architecture for future growth.

Advocate for and adopt cutting-edge data technologies and best practices.

ETL Tool Experience (8+ years):

Extensive experience in designing, developing, and implementing ETL (Extract, Transform, Load) processes using industry-standard tools such as Informatica PowerCenter, IBM DataStage, Microsoft SSIS, or open-source options like Apache Airflow.

Proven ability to build and maintain complex data pipelines that integrate data from diverse sources, transform it into usable formats, and load it into target systems.

Deep understanding of data quality and cleansing techniques to ensure the accuracy and consistency of data across the organization.

Data Governance and Quality:

Establish and enforce a comprehensive data governance framework ensuring data integrity, consistency, and security.

Develop and implement data quality standards and processes for continuous data improvement.

Oversee the implementation of master data management and data lineage initiatives.

Collaboration and Mentorship:

Mentor and guide data teams, including architects, engineers, and analysts, on data architecture principles and best practices.

Foster a collaborative environment where data insights are readily shared and acted upon across the organization.

Build strong relationships with business stakeholders to understand and translate their data needs into actionable solutions.

Qualifications:

Education: master’s degree in computer science, Information Systems, or related field; Ph.D. preferred.

Experience: 25+ years of experience in data architecture and design, with 10+ years in a leadership role.

Technical Skills:

Deep understanding of TOGAF, AWS, MDM, EDW, Hadoop ecosystem (MapReduce, Hive, HBase, Pig, Flume, Scoop), cloud data platforms (Azure Synapse, Google Big Query), modern data pipelines, streaming analytics, data governance frameworks.

Proficiency in programming languages (Java, Python, SQL), scripting languages (Bash, Python), data modelling tools (ER diagramming software), and BI tools.

Extensive expertise in ETL tools (Informatica PowerCenter, IBM DataStage, Microsoft SSIS, Apache Airflow)

Familiarity with emerging data technologies (AI/ML, blockchain), data security and compliance frameworks.

Soft Skills:

Outstanding communication, collaboration, and leadership skills.

Strategic thinking and problem-solving abilities with a focus on delivering impactful solutions.

Strong analytical and critical thinking skills.

Ability to influence and inspire teams to achieve goals.

• 15+ years of Experience in OFSAA Financial Solution Data Foundation and OFSAA Regulatory reporting solutions
• Expert in enterprise solution architecture and design
• Strong understanding in the OFSAA Data Model, Dimension Management and Enterprise Data Warehouse Knowledge.
• Strong Understanding of OFSAA instrument balances reconciliation with General Ledger Summary Level balances
• Experience in defining and build the OFSAA data architecture and sourcing strategy to ensure data accuracy, integrity and quality.
• Understanding of Banking treasury products, US Fed regulatory etc.
• Strong understanding of data lineage. building
• Strong in OFSAA Data Management Tools Knowledge (F2T/T2T/PLT/SCD’s).
• Experience in Business rules configurations in OFSAA framework
• Strong Experience in deploying OFSAA platform (OFSAAI – OFSAA Infrastructure) and installation of OFSAA application - preferably OFSAA 8.x onwards.